Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria
Floods have become a global concern because of the vast economic and ecological havoc that ensue. Thus, a flood risk mitigation strategy is used to reduce flood-related consequences by a long-lead identification of its occurrence. A wide range of causative factors, including the adoption of hybrid m...
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HA Statistics Ndanusa, Ahmed Babalaji Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria |
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Floods have become a global concern because of the vast economic and ecological havoc that ensue. Thus, a flood risk mitigation strategy is used to reduce flood-related consequences by a long-lead identification of its occurrence. A wide range of causative factors, including the adoption of hybrid multi-spatiotemporal data framework is considered in implementing the strategy. Besides the structural or homogenous non-structural factors, the adoption of various Information Systems-based tools are also required to accurately analyse the multiple natural causative factors. Essentially, this was needed to address the inaccurate flood vulnerability classifications and short time of flood prediction. Thus, this study proposes a framework named: Hybrid Multi-spatiotemporal data Framework for Long-lead Upstream Flood Analysis (HyM-SLUFA) to provide a new dimension on flood vulnerability studies by uncovering the influence of multiple factors derived from topography, hydrology, vegetal and precipitation
features towards regional flood vulnerability classification and long-lead analysis. In developing the proposed framework, the spatial images were geometrically and radiometrically corrected with the aid of Quantum Geographic Information System (QGIS). The temporal data were cleaned by means of winsorization methods using STATA statistical tool. The hybrid segment of the framework classifies flood vulnerability and performs long-lead analysis. The classification and analysis were conducted using the corrected spatial images to acquire better understanding on the interaction between the extracted features and rainfall in inducing flood as well as producing various regional flood vulnerabilities within the study area. Additionally, with the aid of regression technique, precipitation and water level data were used to perform long-lead flood analysis to provide a foresight of any potential flooding event in order to take proactive measures. As to confirm the reliability and validity of the proposed framework, an accuracy assessment was conducted on the outputs of the data. This study found the influence of various Flood Causative Factors (FCFs) used in the developed HyM-SLUFA framework, by revealing the spatial disparity indicating
that the slope of a region shows a more accurate level of flood vulnerability compared to other FCFs, which generally causes severe upstream floods when there is low volume of precipitation within regions of low slope degree. Theoretically, the HyM-SLUFA will serve as a guide that can be adopted or adapted for similar studies. Especially, by considering the trend of precipitation and the pattern of flood vulnerability classifications depicted by various FCFs. These classifications will determine the kind(s) of policies that will be implemented in town planning, and the Flood Inducible Precipitation Volumes can provide a foresight of any potential flooding event in order to take practical proactive measures by the local authority. |
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Ndanusa, Ahmed Babalaji |
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Ndanusa, Ahmed Babalaji |
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Ndanusa, Ahmed Babalaji |
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Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria |
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Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria |
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Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria |
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Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria |
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Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria |
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hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of niger state, nigeria |
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Awang Had Salleh Graduate School of Arts & Sciences |
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my-uum-etd.81202022-05-09T07:14:11Z Hybrid approach on multi- spatiotemporal data framework towards analysis of long-lead upstream flood: a case of Niger State, Nigeria 2019 Ndanusa, Ahmed Babalaji Md Dahalin, Zulkhairi Ta’a, Azman Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences HA Statistics Floods have become a global concern because of the vast economic and ecological havoc that ensue. Thus, a flood risk mitigation strategy is used to reduce flood-related consequences by a long-lead identification of its occurrence. A wide range of causative factors, including the adoption of hybrid multi-spatiotemporal data framework is considered in implementing the strategy. Besides the structural or homogenous non-structural factors, the adoption of various Information Systems-based tools are also required to accurately analyse the multiple natural causative factors. Essentially, this was needed to address the inaccurate flood vulnerability classifications and short time of flood prediction. Thus, this study proposes a framework named: Hybrid Multi-spatiotemporal data Framework for Long-lead Upstream Flood Analysis (HyM-SLUFA) to provide a new dimension on flood vulnerability studies by uncovering the influence of multiple factors derived from topography, hydrology, vegetal and precipitation features towards regional flood vulnerability classification and long-lead analysis. In developing the proposed framework, the spatial images were geometrically and radiometrically corrected with the aid of Quantum Geographic Information System (QGIS). The temporal data were cleaned by means of winsorization methods using STATA statistical tool. The hybrid segment of the framework classifies flood vulnerability and performs long-lead analysis. The classification and analysis were conducted using the corrected spatial images to acquire better understanding on the interaction between the extracted features and rainfall in inducing flood as well as producing various regional flood vulnerabilities within the study area. Additionally, with the aid of regression technique, precipitation and water level data were used to perform long-lead flood analysis to provide a foresight of any potential flooding event in order to take proactive measures. As to confirm the reliability and validity of the proposed framework, an accuracy assessment was conducted on the outputs of the data. This study found the influence of various Flood Causative Factors (FCFs) used in the developed HyM-SLUFA framework, by revealing the spatial disparity indicating that the slope of a region shows a more accurate level of flood vulnerability compared to other FCFs, which generally causes severe upstream floods when there is low volume of precipitation within regions of low slope degree. Theoretically, the HyM-SLUFA will serve as a guide that can be adopted or adapted for similar studies. Especially, by considering the trend of precipitation and the pattern of flood vulnerability classifications depicted by various FCFs. These classifications will determine the kind(s) of policies that will be implemented in town planning, and the Flood Inducible Precipitation Volumes can provide a foresight of any potential flooding event in order to take practical proactive measures by the local authority. 2019 Thesis https://etd.uum.edu.my/8120/ https://etd.uum.edu.my/8120/1/s901197_01.pdf text eng public https://etd.uum.edu.my/8120/2/s901197_02.pdf text eng public phd doctoral Universiti Utara Malaysia A. T. Kulkarni, J. Mohanty, T. I. Eldho, E. P. Rao, and B. K. Mohan, “A web GIS based integrated flood assessment modeling tool for coastal urban watersheds,” Comput. Geosci., vol. 64, pp. 7–14, 2014. L. L. Ely, Y. Enzel, V. R. Baker, V. S. Kale, and S. Mishra, “Changes in the magnitude and frequency of late Holocene monsoon floods on the Narmada River, central India,” Bull. Geol. Soc. Am., vol. 108, no. 9, pp. 1134–1148, 1996. J. Wang, S. Yi, M. Li, L. Wang, and C. Song, “Effects of sea level rise, land subsidence, bathymetric change and typhoon tracks on storm flooding in the coastal areas of Shanghai,” Sci. Total Environ., vol. 621, pp. 228–234, 2018. Z. W. Kundzewicz, “Non-structural Flood Protection and Sustainability,” Int. Water Resour. Assoc., vol. 27, no. 1, pp. 3–13, 2002. N. Hazarika, D. Barman, A. K. Das, A. K. Sarma, and S. B. Borah, “Assessing and mapping flood hazard, vulnerability and risk in the Upper Brahmaputra River valley using stakeholders’ knowledge and multicriteria evaluation (MCE),” J. Flood Risk Manag., vol. 11, pp. S700–S716, 2018. W. N. Adger, S. Huq, K. Brown, D. Conway, and M. Hulme, “Adaptation to climate change in the developing world,” Prog. Dev. Stud., vol. 3, no. 3, pp. 179–195, 2003. G. Murray and N. Hojatoleslami, “Disaster Risk Management and Meso-Level Institutions in Nepal,” Southasia Inst. Adv. Stud., 2016. R. Baghel, River Control in India Spatial, Governmental and Subjective Dimensions. India: Springer International Publishing, 2014. I. . Faisal, M. . Kabir, and A. Nishat, “Non-structural flood mitigation measures for Dhaka City,” Urban Water J., vol. 1, no. 2, pp. 145–153, 1999. L. Yohe, Societal adaptation to climate variability, 1st ed. United States: Springer US, 2000. S. Nallan, L. Armstrong, B. Croke, and A. K. Tripathy, “Geospatial data pre-processing on watershed datasets : A GIS approach,” pp. 328–336, 2014. S. V and M. A, “Multispectral Image Pre-Processing for Interactive Satellite Image Classification,” Digit. Earth Summit Geoinformatics, no. May, 2008. D. S. S. Baboo and M. R. Devi, “Geometric Correction in Recent High Resolution Satellite Imagery: A Case Study in Coimbatore, Tamil Nadu,” Int. J. Comput. Appl., vol. 14, no. 1, pp. 32–37, 2011. F. Sillion, Acquisition and display of real-time atmospheric data on terrain, 1st ed. Vienna: Springer-Verlag Wien GmbH, 2001. A. Baraldi and A. Baraldi, “Impact of Radiometric Calibration and Specifications of Spaceborne Optical Imaging Sensors on the Development of Operational Automatic Remote Sensing Image Understanding Systems,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 2, no. 2, pp. 104–134, 2009. C. Samela, R. Albano, A. Sole, and S. Manfreda, “A GIS tool for cost-effective delineation of flood-prone areas,” Comput. Environ. Urban Syst., vol. 70, 43–52, no. July 2018, pp. 1–10, 2018. G. Schumann et al., “High-resolution 3-D flood information from radar imagery for flood hazard management,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 6, pp. 1715–1725, 2007. C. Yang, Y. Wei, S. Wang, Z. Zhang, and S. Huang, “Extracting the flood extent from satellite SAR image with the support of topographic data,” 2001 Int. Conf. Info-Tech Info-Net A Key to Better Life, ICII 2001 - Proc., vol. 1, pp. 87–92, 2001. R. Harris and I. Baumann, “Open data policies and satellite Earth observation,” Space Policy, vol. 32, pp. 44–53, 2015. S. B. Serpico, S. Dellepiane, G. Boni, G. Moser, E. Angiati, and R. Rudari, “Information extraction from remote sensing images for flood monitoring and damage evaluation,” Proc. IEEE, vol. 100, no. 10, pp. 2946–2970, 2012. N. Wanders, D. Karssenberg, A. De Roo, S. M. De Jong, and M. F. P. Bierkens, “The suitability of remotely sensed soil moisture for improving operational flood forecasting,” Hydrol. Earth Syst. Sci., vol. 18, no. 6, pp. 2343–2357, 2014. R. P. Light, D. E. Polley, and K. Börner, “Open data and open code for big science of science studies,” Scientometrics, vol. 101, no. 2, pp. 1535–1551, 2014. I. McCallum et al., “Technologies to Support Community Flood Disaster Risk Reduction,” Int. J. Disaster Risk Sci., vol. 7, no. 2, pp. 198–204, 2016. S. A. Padhye and P. P. Rege, “Feature extraction from microwave data using backscatter coefficient,” 2015 Int. Conf. Ind. Instrum. Control. ICIC 2015, no. Icic, pp. 789–794, 2015. A. M. Youssef, B. Pradhan, and A. M. Hassan, “Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery,” Environ. Earth Sci., vol. 62, no. 3, pp. 611–623, 2011. H. D. Guo, L. Zhang, and L. W. Zhu, “Earth observation big data for climate change research,” Adv. Clim. Chang. Res., vol. 6, no. 2, pp. 108–117, 2015. K. B. Jones, “Importance of land cover and biophysical data in landscape-based environmental assessments,” Am. Assoc. Geogr. Spec. Publ., pp. 215–250, 2008. V. Röthlisberger, A. P. Zischg, and M. Keiler, “Identifying spatial clusters of flood exposure to support decision making in risk management,” Sci. Total Environ., vol. 598, pp. 593–603, 2017. M. Ikusemoran, M. S. Kolawole, and A. K. Martins, “Terrain Analysis for Flood Disaster Vulnerability Assessment : A Case Study of Niger State , Nigeria,” Am. J. Georaphic Inf. Syst., vol. 3, no. 3, pp. 122–134, 2014. S. Graves, R. Ramachandran, and T. Berendes, “Using GLIDER for Knowledge Discovery in Climate Science to Visualize , Analyze and Mine Satellite Imagery,” pp. 488–494, 2013. T. R. Gowda and G. R. Vijay, “Effective Analysis of Data from Remote Sensing Application,” Int. J. Comput. Eng. Res. Trends, vol. 3, no. 6, pp. 279–283, 2016. E. Schnebele and G. Cervone, “Improving remote sensing flood assessment using volunteered geographical data,” Nat. Hazards Earth Syst. Sci., vol. 13, no. 3, pp. 669–677, 2013. J. F. Rosser, D. G. Leibovici, and M. J. Jackson, “Rapid flood inundation mapping using social media, remote sensing and topographic data,” Nat. Hazards, vol. 87, no. 1, pp. 103–120, 2017. A. M et al., “Flood Impact Assessment in Kota Bharu, Malaysia: A Statistical Analysis,” World Appl. Sci. Journa, vol. 32, no. 4, pp. 626–634, 2014. I. O. Adelekan, “Vulnerability assessment of an urban flood in Nigeria: Abeokuta flood 2007,” Nat. Hazards, vol. 56, no. 1, pp. 215–231, 2011. J. M. Martinez and T. Le Toan, “Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data,” Remote Sens. Environ., vol. 108, no. 3, pp. 209–223, 2007. L. C. SMITH, “Satellite remote sensing of river inundation area, stage, and discharge: a review,” Hydrol. Process., vol. 11, no. 10, pp. 1427–1439, 1997. S. K. Jain, R. D. Singh, M. K. Jain, and A. K. Lohani, “Delineation of flood-prone areas using remote sensing techniques,” Water Resour. Manag., vol. 19, no. 4, pp. 333–347, 2005. R. Abah and Clement, “An application of Geographic Information System in mapping flood risk zones in a north central city in Nigeria,” African J. Environ. Sci. Technol., vol. 7, no. 6, pp. 365–371, 2013. C. O. Dumitru, S. Cui, D. Faur, and M. Datcu, “Data analytics for rapid mapping: Case study of a flooding event in Germany and the tsunami in Japan using very high resolution SAR images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, no. 1, pp. 114–129, 2015. S. Voigt, T. Kemper, T. Riedlinger, R. Kiefl, K. Scholte, and H. Mehl, “Satellite Image Analysis for Disaster and Crisis-Management Support,” vol. 45, no. 6, pp. 1520–1528, 2007. D. Kim, Y. Sun, D. Wendi, Z. Jiang, S. Liong, and P. Gourbesville, “Flood Modelling Framework for Kuching City , Malaysia : Overcoming the Lack of Data,” 2018. Y. M. Abui, S. O. Akoh, A. Bako, and A. S. O. O, “Anthropogenic activities and flood inducement in Kubwa Urban Environment Federal Capital , Nigeria,” Int. J. Environ. Sci. Technol., vol. 1, no. May, pp. 31–38, 2016. Y. Ma, L. Wang, P. Liu, and R. Ranjan, “Towards building a data-intensive index for big data computing - A case study of Remote Sensing data processing,” Inf. Sci. (Ny)., vol. 319, pp. 171–188, 2015. N. Kettaneh, A. Berglund, and S. Wold, “PCA and PLS with very large data sets,” Comput. Stat. Data Anal., vol. 48, no. 1, pp. 69–85, 2005. J. M. Hellerstein, “Quantitative Data Cleaning for Large Databases,” United Nations Econ. Comm. Eur., p. 42, 2008. H. Wu, H. Zhang, J. Zhang, and F. Xu, “Typical Target Detection in Satellite Images Based on Convolutional Neural Networks,” 2015 IEEE Int. Conf. Syst. Man, Cybern., pp. 2956–2961, 2015. E. Gaume, M. Livet, M. Desbordes, and J. P. Villeneuve, “Hydrological analysis of the river Aude, France, flash flood on 12 and 13 November 1999,” J. Hydrol., vol. 286, no. 1–4, pp. 135–154, 2004. C. Armenakis, E. Du, S. Natesan, R. Persad, and Y. Zhang, “Flood Risk Assessment in Urban Areas Based on Spatial Analytics and Social Factors,” Geosciences, vol. 7, no. 4, p. 123, 2017. F. Pappenberger et al., “Cascading model uncertainty from medium range weather forecasts ( 10 days ) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System ( EFFS ) To cite this version : HAL Id : hal-00304846 Cascading model ,” 2005. F. Ahmat, A. M. Samad, M. Tajjudin, and R. Adnan, “Flood Water Level Prediction Modeling Using NNARX Structure for Sg Pahang Basin,” no. November, pp. 27–29, 2015. M. Mulligan and G. A. Blackburn, “A real-time hydrological model for flood prediction using GIS and the WWW,” vol. 27, pp. 9–32, 2003. T. H. Bich et al., “Impacts of flood on health: epidemiologic evidence from Hanoi, Vietnam,” vol. 1, pp. 1–8, 2011. F. A. Ruslan, A. M. Samad, and R. Adnan, “Modelling of Flood Prediction System Using Hybrid NNARX and Extended Kalman Filter,” no. March, pp. 10–12, 2017. Y. Di, W. Ding, Y. Mu, D. L. Small, and S. Islam, “Developing Machine Learning Tools for Long-Lead Heavy Precipitation Prediction with Multi-sensor Data,” pp. 63–68, 2015. Y. Zhuang, K. Yu, D. Wang, and W. Ding, “An Evaluation of Big Data Analytics in Feature Selection for Long-lead Extreme Floods Forecasting,” 2016. H. M. Noor, D. Ndzi, G. Yang, N. Zuraidin, and M. Safar, “Rainfall-based River Flow Prediction Using NARX in Malaysia,” no. March, pp. 10–12, 2017. B. W. Golding, “Long lead time flood warnings: reality or fantasy?. Meteorological Applications,” Meteorol. Appl., vol. 16, no. January, pp. 3–12, 2009. R. Adnan, A. M. Samad, Z. Zain, and F. A. Ruslan, “5 Hours Flood Prediction Modeling Using Improved NNARX Structure : Case Study Kuala Lumpur,” pp. 5–9, 2014. F. A. Ruslan, A. M. Samad, M. Tajjudin, and R. Adnan, “7 Hours Flood Prediction Modelling Using NNARX Structure: Case Study Terengganu *,” no. March, pp. 4–6, 2016. M. S. Tehrany, B. Pradhan, S. Mansor, and N. Ahmad, “Flood susceptibility assessment using GIS-based support vector machine model with different kernel types,” Catena, vol. 125, pp. 91–101, 2015. F. Ulyhu et al., “River basin flood prediction using support vector machines,” Int. Jt. Conf. Neural Networks-IEEE, pp. 3039–3043, 2008. A. Yusoff, N. Md Din, S. Yussof, and S. U. Khan, “Big data analytics for flood information management in Kelantan, Malaysia,” 2015 IEEE Student Conf. Res. Dev. Kuala Lumpur, Malaysia, vol. 20, no. 20, pp. 1–6, 2015. C. H. Yu, D. Luo, W. Ding, J. Cohen, D. Small, and S. Islam, “Spatio-temporal asynchronous co-occurrence pattern for big climate data towards long-lead flood prediction,” Proc. - 2015 IEEE Int. Conf. Big Data, IEEE Big Data 2015, no. October, pp. 865–870, 2015. M. Sangati, M. Borga, D. Rabuffetti, and R. Bechini, “Influence of rainfall and soil properties spatial aggregation on extreme flash flood response modelling: An evaluation based on the Sesia river basin, North Western Italy,” Adv. Water Resour., vol. 32, no. 7, pp. 1090–1106, 2009. R. S. Lunetta and J. G. Lyon, Remote Sensing and GIS Accuracy Assessment (Mapping Science). 2004. N. Mustafee, P. Godsiff, D. Baudry, A. Smart, and A. Louis, “Investigating Execution Strategies for Hybrid Models developed using Multiple M & S Methodologies,” 2015 Spring Simul. Multi-Conference, pp. 78–85, 2015. P. Kumar, S., & Phrommathed, “Research methodology,” Springer US, 2005, pp. 43–50. F. A. Ruslan, A. M. Samad, Z. Zain, and R. Adnan, “Flood Water Level Modeling and Prediction Using NARX Neural Network : Case Study at Kelang River,” no. 1, pp. 7–9, 2014. F. Rengers, L. Mcguire, J. Kean, and D. Staley, “Flood and Debris Flow Hazard Predictions in Steep , Burned Landscapes,” vol. 18, p. 2658, 2016. Y. Lee and S. D. Brody, “Examining the impact of land use on flood losses in Seoul, Korea,” Land use policy, vol. 70, no. February 2017, pp. 500–509, 2018. W. J. Junk et al., “Brazilian wetlands : their de fi nition , delineation , and classi fi cation for research , sustainable management , and protection,” 2013. A. Domeneghetti et al., “Flood risk mitigation in developing countries: Deriving accurate topographic data for remote areas under severe time and economic constraints,” J. Flood Risk Manag., vol. 8, no. 4, pp. 301–314, 2015. S. A. Nelson, “River Flooding,” Tulane University, 2015. Centre for Research on the Epidemiology of Disasters (CRED), “The human cost of weather-related disasters, 1995–2015,” The human cost of weather-related disasters, 2015. . Q. Government, “Office of the Queensland Chief Scientist,” 2016. . C. G. Smyth and S. A. Royle, “Urban landslide hazards: incidence and causative factors in Niterói, Rio de Janeiro State, Brazil,” Appl. Geogr., vol. 20, no. 2, pp. 95–118, 2000. R. Roslee, F. Tongkul, N. Simon, and M. N. Norhisham, “Flood Potential Analysis ( FPAn ) using Geo-Spatial Data in Penampang area , Sabah Malaysian Journal of Geosciences,” vol. 01, no. 1, pp. 1–6, 2017. D. R. Maidment, “GIS and Hydrologic Modeling - an Assessment of Progress,” International Conference on GIS and Environmental Modeling, 1996. . S. Savary, a N. Rousseau, and R. Quilbe, “Assessing the Effects of Historical Land Cover Changes on Runoff and Low Flows Using Remote Sensing and Hydrological Modeling,” J. Hydrol. Eng., vol. 14, no. 6, pp. 575–587, 2009. O. Ahlqvist, D. E. Varanka, S. Fritz, and K. Janowicz, Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects. 2015. C. P. Giri, Remote sensing of land use and land cover : principles and applications. 2016. A. F. Wall, Y. Yanes, J. H. Miller, and A. I. Miller, “Bellwether of the Canaries: anthropogenic effects on the land snail fauna of the Canary Islands,” Biodivers. Conserv., vol. 27, no. 2, pp. 395–415, 2018. P. Keddy, Wetland Ecology Principles and Conservation, Second., vol. 21, no. 3. 2001. A. Gires, J. B. Abbes, I. da Silva Rocha Paz, I. Tchiguirinskaia, and D. Schertzer, “Multifractal characterisation of a simulated surface flow: A case study with Multi-Hydro in Jouy-en-Josas, France,” J. Hydrol., vol. 558, pp. 482–495, 2018. B. S. Agbola, O. Ajayi, O. J. Taiwo, and B. W. Wahab, “The August 2011 flood in Ibadan, Nigeria: Anthropogenic causes and consequences,” Int. J. Disaster Risk Sci., vol. 3, no. 4, pp. 207–217, 2012. T. T. Kozlowski, and Plant Growth. 1984. S. K. Roger Few, Franziska Matthies, Mike Ahern, Flood hazards and health: responding to present and future risks, 1st ed. London, United Kingdom: Taylor & Francis., 2013. D. A. Bossio and K. M. Scow, “Impacts of carbon and flooding on soil microbial communities: Phospholipid fatty acid profiles and substrate utilization patterns,” Microb. Ecol., vol. 35, no. 3, pp. 265–278, 1998. Y. M. Abui, S. O. Akoh, A. Bako, and A. S. O. O, “Anthropogenic activities and flood inducement in Kubwa Urban Environment Federal Capital , Nigeria,” vol. 1, no. May, pp. 31–38, 2016. A. A. Ezekiel, T. A. Adebayo, A. O. Adewoye, and A. Taiwo, “Effect of Floods Explosion and Chemical Action on Fish Farming in Ibadan Metropolis, Oyo State, Nigeria,” vol. 6, no. 1, pp. 62–76, 2016. S. A. N. Nirupama, “SHORT COMMUNICATION A serious flooding event in Nigeria in 2012 with specific focus on Benue State : a brief review,” no. Nimet 2012, 2015. I. M. Adekunle, M. T. Adetunji, A. M. Gbadebo, and O. P. Banjoko, “Assessment of groundwater quality in a typical rural settlement in southwest Nigeria.,” Int. J. Environ. Res. Public Health, vol. 4, no. 4, pp. 307–318, 2007. J. P. Unyimadu, O. Osibanjo, and J. O. Babayemi, “Selected persistent organic pollutants (POPs) in water of River Niger: occurrence and distribution.,” Environ. Monit. Assess., vol. 190, no. 1, p. 6, 2017. W. Yusuf, O. and Nwachukwu, “Technical Efficiency of Institute For Agricultural Research (IAR) Developed Variety Of Cowpea (Sampea 11) Production In Niger State, Nigeria,” pp. 194–205, 2015. M. Dalil, N. H. Mohammad, U. M. Yamman, and A. Husaini, “An assessment of flood vulnerability on physical development along drainage channels in Minna , Niger,” vol. 9, no. January, pp. 38–46, 2015. G. Titilope and O. Fabien, “Hydro-climatic changes in the Niger basin and consistency of local perceptions,” Reg. Environ. Chang., vol. 15, no. 8, pp. 1627–1637, 2014. I. B. U. Mohammed Chado Isah, Muideen Dawud Oladipupo, Alhassan Usman Gabi, “Quality Assessment of Fresh and Frozen Clarias gariepinus and Tilapia zillii from Shiroro and Tagwai Dam Reservoirs in Niger State, Nigeria,” J. Sci. Technol., p. 2012, 2012. K. Burningham, S. Lecturer, J. Fielding, and D. Thrush, “‘ It ’ ll never happen to me ’: understanding public awareness of local flood risk,” Disasters, vol. 32, no. 2, pp. 216–238, 2008. S. H. Cannon, J. E. Gartner, R. C. Wilson, J. C. Bowers, and J. L. Laber, “Storm rainfall conditions for floods and debris flows from recently burned areas in southwestern Colorado and southern California,” Geomorphology, vol. 96, no. 3–4, pp. 250–269, 2008. P. Adhikari et al., “A digitized global flood inventory (1998-2008): Compilation and preliminary results,” Nat. Hazards, vol. 55, no. 2, pp. 405–422, 2010. M. A. R. Shah, A. Rahman, and S. H. Chowdhury, “Challenges for achieving sustainable flood risk management,” J. Flood Risk Manag., vol. 11, no. 1, pp. 352–358, 2018. P. Sayers et al., Flood Risk Management: A Strategic Approach. Paris: UNESCO, 2013. Y. F. Sang and M. Yang, “Urban waterlogs control in China: more effective strategies and actions are needed,” Nat. Hazards, vol. 85, no. 2, pp. 1291–1294, 2017. F. K. S. Chan, C. C. Joon, A. D. Ziegler, M. Dabrowski, and O. Varis, “Towards resilient flood risk management for Asian coastal cities: lessons learned from Hong Kong and Singapore,” J. Clean. Prod., 2018. J. F. Warner, M. F. van Staveren, and J. van Tatenhove, “Cutting dikes, cutting ties? Reintroducing flood dynamics in coastal polders in Bangladesh and the netherlands,” Int. J. Disaster Risk Reduct., 2018. D. O. Olukanni, A. Adejumo, and W. Salami, “Assessment of jebba hydropower dam operation for improved energy production and flood management,” ARPN J. Eng. Appl. Sci., vol. 11, no. 13, pp. 8450–8467, 2016. C. E. Haque and I. Burton, “Adaptation options strategies for hazards and vulnerability mitigation: An international perspective,” Mitig. Nat. Hazards Disasters Int. Perspect., pp. 3–21, 2005. M. Abdul Mohit and G. Mohamed Sellu, “Development of Non-structural Flood Mitigation Policies and Measures for Pekan town, Malaysia,” Asian J. Behav. Stud., vol. 2, no. 6, p. 9, 2017. Andjelkovic, “GUIDELINESONNON-STRUCTURAL MEASURES INURBAN FLOOD MANAGEMENT,” International Hydrological Programme, 2001. . S. C. Anih, “Effective Survival measures against Natural hazards in settled areas,” Manag. Environ. Probl. Hazards Niger., vol. 2, no. 3, pp. 55–58, 2017. T. K. S. Abam, “Impact of dams on the hydrology of the Niger,” pp. 239–251, 1999. R. J. Dawson, T. Ball, J. Werritty, A. Werritty, J. W. Hall, and N. Roche, “Assessing the effectiveness of non-structural flood management measures in the Thames Estuary under conditions of socio-economic and environmental change,” Glob. Environ. Chang., vol. 21, no. 2, pp. 628–646, 2011. S. V. Shivaprasad Sharma, R. Parth Sarathi, V. Chakravarthi, G. Srinivasarao, and V. Bhanumurthy, “Extraction of detailed level flood hazard zones using multi-temporal historical satellite data-sets–a case study of Kopili River Basin, Assam, India,” Geomatics, Nat. Hazards Risk, vol. 8, no. 2, pp. 792–802, 2017. D. A. Quattrochi, Elizabeth A. Wentz, N. S.-N. Lam, and C. W. Emerson, Integrating Scale in Remote Sensing and GIS., 1st ed. New York: CRC Press, 2017. Å. Rinnan, F. van den Berg, and S. B. Engelsen, “Review of the most common pre-processing techniques for near-infrared spectra,” TrAC - Trends Anal. Chem., vol. 28, no. 10, pp. 1201–1222, 2009. J. Engel et al., “Breaking with trends in pre-processing?,” TrAC - Trends Anal. Chem., vol. 50, pp. 96–106, 2013. M. Sarode and P. Deshmukh, “Reduction of speckle noise and image enhancement of images using filtering technique,” Int. J. Adv. …, vol. 2, no. 1, pp. 30–38, 2011. N. G. Kardoulas, a C. Bird, and a I. Lawan, “Geometric correction of SPOT and landsat imagery: A comparison of map- and GPS-derived control points,” Photogramm. Eng. Remote Sensing, vol. 62, no. 10, pp. 1173–1177, 1996. E. Tomppo and M. Katila, “Satellite image-based national forest inventory of finland for publication in the igarss’91 digest,” [Proceedings] IGARSS’91 Remote Sens. Glob. Monit. Earth Manag., vol. 3, pp. 1141–1144, 1991. Y. Zhao, Y. Tan, and W. Xia, “Development of China’s independent satellite data preprocessing system based on ArcEngine and IDL,” Geomatics Integr. Water Resour. Manag. IEEE., pp. 1–4, 2012. B. Büchele Kreibich, H., Kron, A., Thieken, A., Ihringer, J., Oberle, P., Merz, B., and Nestmann, F., “Flood-risk mapping: contributions towards an enhanced assessment\rof extreme events and associated risks,” Nat. Hazards Earth Syst. Sci., vol. 6, no. Methods for risk assessment and mapping in Germany, pp. 485–503, 2006. K. Uddin, D. R. Gurung, A. Giriraj, and B. Shrestha, “Application of Remote Sensing and GIS for Flood Hazard Management : A Case Study from Sindh Province ,” vol. 2, no. September 1988, pp. 1–5, 2013. and A. R. Roy, Ciya Maria, Elsa Manoj, Harsha Joy, Sarin Ravi, “Development of flood hazard vulnerability map for alappuzha district.,” Tech. Res. Organ., vol. 5, no. 3, pp. 77–81, 2018. O. Rahmati, H. Zeinivand, and M. Besharat, “Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis,” Geomatics, Nat. Hazards Risk, vol. 7, no. 3, pp. 1000–1017, 2016. S. H. Pourali, C. Arrowsmith, N. Chrisman, A. A. Matkan, and D. Mitchell, “Topography Wetness Index Application in Flood-Risk-Based Land Use Planning,” 2014. P. K. Garg and R. D. Garg, “Geospatial techniques for flood inundation mapping,” Geosci. Remote Sens. Symp., pp. 4387–4390, 2016. Y. L. Zhou, Guiyun, Junjie Zhou, “Filling depressions based on sub-watersheds in raster digital elevation models,” Geosci. Remote Sens. Symp., pp. 6071–6074, 2017. A. Tariq, S. M. Osama, and A. Gillani, “Development of a Low Cost and Light Weight UAV for Photogrammetry and Precision Land Mapping Using Aerial Imagery,” Proc. - 14th Int. Conf. Front. Inf. Technol. FIT 2016, pp. 360–364, 2017. S. H. Lai, P. L. Law, and Y. S. Mah, “Applications of gis and remote sensing in the hydrological study of the upper bernam river basin , Malaysia,” pp. 13–18, 2007. A. J. Tatem, H. G. Lewis, P. M. Atkinson, and M. S. Nixon, “Super-resolution target identification from remotely sensed images using a Hopfield neural network,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 4, pp. 781–796, 2001. M. Kopecký and Š. Čížková, “Using topographic wetness index in vegetation ecology: Does the algorithm matter?,” Appl. Veg. Sci., vol. 13, no. 4, pp. 450–459, 2010. S. Dellepiane, R. De Laurentiis, and F. Giordano, “Coastline extraction from SAR images and a method for the evaluation of the coastline precision,” Pattern Recognit. Lett., vol. 25, no. 13, pp. 1461–1470, 2004. Y. Kwak, J. Park, and K. Fukami, “Near real-time flood volume estimation from MODIS time-series imagery in the indus river basin,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 7, no. 2, pp. 578–586, 2014. P. Nakmuenwai and F. Yamazaki, “Extraction of flooded areas in the 2011 Thailand flood from RADARSAT-2 and ThaiChote images,” Int. Geosci. Remote Sens. Symp., pp. 3354–3357, 2014. D. Razafipahatelo, S. Rakotoniaina, and S. Rakotondraompiana, “Automatic floods detection with a kernel k-means approach,” 2014 IEEE Canada Int. Humanit. Technol. Conf. IHTC 2014, vol. 2, pp. 1–4, 2014. F. Nirchio, “Speditive COSMO-SkyMed SAR quick-look analysis for change detection in support to disaster management and environmental monitoring,” Int. Geosci. Remote Sens. Symp., vol. 2015–Novem, pp. 4809–4812, 2015. H. Linke, D. Karimanzira, T. Rauschenbach, and T. Pfützenreuter, “Flash flood prediction for small rivers,” no. April, pp. 11–13, 2011. F. Pappenberger, J. Bartholmes, J. Thielen, H. L. Cloke, R. Buizza, and A. de Roo, “New dimensions in early flood warning across the globe using grand-ensemble weather predictions,” Geophys. Res. Lett., vol. 35, no. 10, pp. 1–7, 2008. I. Yucel, A. Onen, K. K. Yilmaz, and D. J. Gochis, “Calibration and evaluation of a flood forecasting system : Utility of numerical weather prediction model , data assimilation and satellite-based rainfall,” J. Hydrol., vol. 523, pp. 49–66, 2015. J. Dukiya, “Geostatistics : An Overview Spatial Analysis of the Impacts of Kainji Hydropower Dam on the Down Stream Communities,” vol. 7, pp. 1–5, 2013. F. A. Ruslan, A. M. Samad, Z. Zain, and R. Adnan, “Flood Prediction using NARX Neural Network and EKF Prediction Technique : A Comparative Study,” pp. 19–20, 2013. P. Taylor et al., “Multi-step-ahead neural networks for flood forecasting Multi-step-ahead neural networks for flood forecasting,” no. December 2014, pp. 37–41, 2010. B. A. Olumide, M. Saidu, and A. Oluwasesan, “Evaluation of Best Fit Probability Distribution Models for the Prediction of Rainfall and Runoff Volume ( Case Study Tagwai Dam , Minna-Nigeria ),” vol. 3, no. 2, pp. 94–98, 2013. L. Lu, S. Zhang, J. Yu, and H. Zhou, “Short-term Water Level Prediction using Different Artificial Intelligent Models,” Agro-Geoinformatics (Agro-Geoinformatics), 2016 Fifth Int. Conf. IEEE, p. 16. M. Azrol, S. Anuar, R. Z. Abdul, S. B. Mohd, and A. C. Soh, “Early Prediction System Using Neural Network in Kelantan River , Malaysia,” 2017. A. Ali, Mechanisms , Impacts , and Management. Pakistan: Cataloging-In-Publication Data, 2013. M. T. B. Dalu, C. M. Shackleton, and T. Dalu, “Influence of land cover, proximity to streams and household topographical location on flooding impact in informal settlements in the Eastern Cape, South Africa,” Int. J. Disaster Risk Reduct., 2018. N. W. Chan and N. W. Chan, “Flood disaster management in Malaysia : an evaluation of the effectiveness of government resettlement schemes,” 2006. P. Taylor, D. Koutsoyiannis, and D. Koutsoyiannis, “Statistics of extremes and estimation of extreme rainfall : I . Theoretical investigation / Statistiques de valeurs extrêmes et estimation de précipitations extrêmes : I . Recherche théorique Statistics of extremes and estimation of extreme rainfall : I .,” no. April 2013, pp. 37–41, 2009. M. F. Goodchild, M. Yuan, and T. J. Cova, “Towards a general theory of geographic representation in GIS,” vol. 8816, 2007. R. Kumar, Research Methodology: a step by step guide for beginners., 3rd ed., vol. 109, no. 1. London, United Kingdom, 2011. Mallika Rao and Shweta Shah, “EXPERIMENTATIONA RESEARCH METHODOLOGY,” 2002. A. Refice, A. D’Addabbo, and D. Capolongo, Flood Monitoring through Remote Sensing, 1st ed. Italy: Springer International Publishing, 2018. K. Thapa and J. Bossler, “Accuracy of Spatial Data Used In Geographic Information Systems,” Photogramm. Eng. Remote Sens., vol. 58, no. 6, pp. 835–841, 1992. S. N. M. Zad, Z. Zulkafli, and F. M. Muharram, “Satellite rainfall (TRMM 3B42-V7) performance assessment and adjustment over Pahang river basin, Malaysia,” Remote Sens., vol. 10, no. 3, pp. 1–24, 2018. D. Pflugmacher, W. B. Cohen, R. E. Kennedy, and Z. Yang, “Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics,” Remote Sens. Environ., vol. 151, pp. 124–137, 2014. C. Cruzen, M. Schmidhuber, Y. H. Lee, and B. Kim, Space Operations: Contributions from the Global Community. Alabama, USA: Springer International Publishing, 2017. S. Jason et al., “Capacity building in emerging space nations: Experiences, challenges and benefits,” Adv. Sp. Res., vol. 46, no. 5, pp. 571–581, 2010. A. Cawthorne, M. Beard, A. Carrel, G. Richardson, and A. Lawal, “SSC08-III-7,” Surrey Res. Park, pp. 1–8, 2009. G. K. James, J. Akinyede, and S. A. Halilu, “The Nigerian Space Program and Its Economic Development Model,” New Sp., vol. 2, no. 1, pp. 23–29, 2014. J. Chen et al., “Big data challenge: A data management perspective,” Front. Comput. Sci., vol. 7, no. 2, pp. 157–164, 2013. M. Sujatha, G. Lavanya Devi, K. Srinivasa Rao, and N. Ramesh, “Rough Set Theory Based Missing Value Imputation,” pp. 97–106, 2018. Y. Ma et al., “Remote sensing big data computing: Challenges and opportunities,” Futur. Gener. Comput. Syst., vol. 51, pp. 47–60, 2015. P. Gamba, P. Du, C. Juergens, and D. Maktav, “Foreword to the Special Issue on “Human Settlements: A Global Remote Sensing Challenge”,” Sel. Top. Appl. Earth Obs. Remote Sensing, IEEE J., vol. 4, no. 1, pp. 5–7, 2011. a. Rosenqvist et al., “An overview of the JERS-1 SAR Global Boreal Forest Mapping (GBFM) project,” IGARSS 2004. 2004 IEEE Int. Geosci. Remote Sens. Symp., vol. 2, no. C, pp. 1–4, 2004. R. Bryant, R. Katz, and E. Lazowska, “Big-Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science and Society,” Comput. Res. Assoc., pp. 1–15, 2008. A. H. Goudarzi and N. Ghadiri, “A hybrid spatial data mining approach based on fuzzy topological relations and MOSES evolutionary algorithm,” arXiv Prepr. arXiv1704.06621, no. April 2017, pp. 1–25, 2017. C. L. Philip Chen and C. Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Inf. Sci. (Ny)., vol. 275, pp. 314–347, 2014. M. Faridi, S. Verma, and S. Mukherjee, “Integration of GIS, Spatial Data Mining, and Fuzzy Logic for Agricultural Intelligence BT - Soft Computing: Theories and Applications,” Soft Comput. Theor. Appl. Adv. Intell. Syst. Comput., pp. 171–183, 2018. C. Leek and C. Leek, “Information systems frameworks and strategy,” Ind. Manag. Data Syst., vol. 97, no. 3, pp. 86–89, 1997. H. Chen, W.-S. Ku, H. Wang, and M.-T. Sun, “Leveraging spatio-temporal redundancy for RFID data cleansing,” Proc. 2010 Int. Conf. Manag. data - SIGMOD ’10, pp. 51–62, 2010. P. Agrawal, A. D. Sarma, J. Ullman, and J. Widom, “Foundations of Uncertain-Data Integration,” To Appear Proc. 36th Int. Conf. Very Large Data Bases, pp. 1–24, 2010. P. L. N. Raju, “Fundamentals of Geographical Information System,” Satell. Remote Sens. GIS Appl. Agric. Meteorol., vol. 1, pp. 103–120, 2015. L. Lin, T. Peng, and J. Kennedy, “A rule based taxonomy of dirty data,” J. Comput., vol. 1, no. 2, pp. 140–148, 2011. D. K. Mishra and W. SHI, Information and Communication Technology for Sustainable Development, 1st ed., vol. 10. Warsaw, Poland: Springer, 2016. H. Liu, A. K. Tk, J. P. Thomas, and X. Hou, “Cleaning Framework for BigData: An Interactive Approach for Data Cleaning,” Proc. - 2016 IEEE 2nd Int. Conf. Big Data Comput. Serv. Appl. BigDataService 2016, pp. 174–181, 2016. S. Basu, S. Ganguly, S. Mukhopadhyay, R. DiBiano, M. Karki, and R. Nemani, “DeepSat - A Learning framework for Satellite Imagery,” Proc. 23rd Sigspatial Int. Conf. Adv. Geogr. Inf. Syst., pp. 3–7, 2015. D. Frantz, A. Röder, M. Stellmes, and J. Hill, “An operational radiometric landsat preprocessing framework for large-area time series applications,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 7, pp. 3928–3943, 2016. M. Egmont-Petersen, D. de Ridder, and H. Handels, “Image processing with neural networks—a review,” Pattern Recognit., vol. 35, no. 10, pp. 2279–2301, 2002. I. Keramitsoglou, C. Cartalis, and C. T. Kiranoudis, “Automatic identification of oil spills on satellite images,” Environ. Model. Softw., vol. 21, no. 5, pp. 640–652, 2006. S. Zhang, Q. Yang, C. Zhang, and M. City, “Proceedings of the First International Workshop on Data Cleaning and Preprocessing,” 2002, pp. 88–92. P. M. Teillet, “Image correction for radiometric effects in remote sensing,” Int. J. Remote Sens., vol. 7, no. 12, pp. 1637–1651, 1986. Y. Du, P. M. Teillet, and J. Cihlar, “Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection,” Remote Sens. Environ., vol. 82, no. 1, pp. 123–134, 2002. X. Pons, L. Pesquer, J. Cristóbal, and O. González-Guerrero, “Automatic and improved radiometric correction of landsat imageryusing reference values from MODIS surface reflectance images,” Int. J. Appl. Earth Obs. Geoinf., vol. 33, no. 1, pp. 243–254, 2014. P. R. Coppin and M. E. Bauer, “Digital change detection in forest ecosystems with remote sensing imagery,” Remote Sens. Rev., vol. 13, no. 3–4, pp. 207–234, 2009. K. C. Tan, H. S. Lim, M. Z. MatJafri, and K. Abdullah, “A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery,” Environ. Monit. Assess., vol. 184, no. 6, pp. 3813–3829, 2012. D. Lu, P. Mausel, E. Brondizio, and E. Moran, “Assessment of atmospheric correction methods for landsat tm data applicable to amazon basin lba research,” Int. J. Remote Sens., vol. 23, no. 13, pp. 2651–2671, 2002. Z. Sun, R. Ma, and Y. Wang, “Using Landsat data to determine land use changes in Datong basin, China,” Environ. Geol., vol. 57, no. 8, pp. 1825–1837, 2009. R. Binet, K. W. Lewis, O. Aharonson, and J. Avouac, “Registration and Change Detection Applications,” Div. Geol. Planet. Sci. Calif. Inst. Technol., pp. 1072–1075, 2008. G. E. Ford, “Analysis and quantification of errors in the geometric correction of satellite images,” Photogramm. Eng. Remote Sensing, vol. 51, no. 11, pp. 1725–1734, 1985. F. O. Adeola, O. Jolaade, and O. Adeyemi, “An Assessment of Digital Elevation Model for Geospatial Studies : A Case Study of Alawa Town , Niger State , Nigeria,” vol. 15, pp. 31–51, 2017. A. T. Salami, Dynamics of Forest Ecosystems in Central Africa. 2008. A. I. Naibbi and S. S. Ibrahim, “An Assessment of the Existing Continuously Operating Reference Stations ( CORS ) in Nigeria : An Exploration Using Geographical Information System ( GIS ),” Am. J. Geogr. Inf. Syst., vol. 3, no. 4, pp. 147–157, 2014. V. Singh, G. Kumar, and G. Arora, “Textural Features for Image Classification,” IEEE Trans. Syst. Man. Cybern., vol. SMC-3, no. 6, pp. 610–621, 1973. A. K. Bhandari, A. Kumar, and G. K. Singh, “Improved feature extraction scheme for satellite images using NDVI and NDWI technique based on DWT and SVD,” Arab. J. Geosci., vol. 8, no. 9, pp. 6949–6966, 2015. M. Singh, V., Kumar, G., & Arora, G. (2016, “Analytical evaluation for the enhancement of satellite images using swarm intelligence techniques. In Computing for Sustainable Global Development (INDIACom),” 2016 3rd Int. Conf. on. IEEE, vol. 16, p. (pp. 2401-2405), 2016. S. N. Jonkman, “Global perspectives on loss of human life caused by floods,” Nat. Hazards, vol. 34, no. 2, pp. 151–175, 2005. M. Seeger, M. P. Errea, S. Beguería, J. Arnáez, C. Martí, and J. M. García-Ruiz, “Catchment soil moisture and rainfall characteristics as determinant factors for discharge/suspended sediment hysteretic loops in a small headwater catchment in the Spanish pyrenees,” J. Hydrol., vol. 288, no. 3–4, pp. 299–311, 2004. S. Shete et al., “Effect of Winsorization on Power and Type 1 Error of Variance Components and Related Methods of QTL Detection,” Behav. Genet., vol. 34, no. 2, pp. 153–159, 2004. R. M. Flikkema, “Statistical Methodology for Data With Multiple Limits Of Detection,” 2016. A. Purnanandam and D. Weagley, “Can Markets Discipline Government Agencies? Evidence from the Weather Derivatives Market,” J. Finance, vol. 71, no. 1, pp. 303–334, 2016. J. A. Neves, S. de B. Melo, and E. V. de S. B. Sampaio, “An Index of Susceptibility to Drought (ISD) for the Semiarid Brazilian Northeast,” Rev. Bras. Meteorol., vol. 31, no. 2, pp. 177–195, 2016. Mark Nixon and A. Aguado, Feature Extraction & Image Processing for Computer Vision. 2012. J. Senthilnath, S. N. Omkar, V. Mani, R. Prasad, R. Rajendra, and P. B. Shreyas, “Multi-sensor satellite remote sensing images for flood assessment using swarm intelligence,” pp. 1–5, 2015. W. Wang, N. Yang, Y. Zhang, F. Wang, T. Cao, and P. Eklund, “A review of road extraction from remote sensing images,” J. Traffic Transp. Eng. (English Ed., vol. 3, no. 3, pp. 271–282, 2016. A. K. Saraf, P. R. Choudhury, B. Roy, B. Sarma, S. Vijay, and S. Choudhury, “GIS based surface hydrological modelling in identification of groundwater recharge zones,” Int. J. Remote Sens., vol. 25, no. 24, pp. 5759–5770, 2004. I. D. Moore, R. B. Grayson, and a R. Ladson, “Digital Terrain Modeling : A Review of Hydrological Geomorphological and Biological Applications,” Hydrol. Process., vol. 5, no. 1, pp. 3–30, 1991. L. W. Zevenbergen and C. R. Thorne, “Quantitative analysis of land surface topography,” Earth Surf. Process. Landforms, vol. 12, no. 1, pp. 47–56, 1987. H. Cai, W. Rasdorf, and C. Tilley, “Approach to Determine Extent and Depth of Highway Flooding,” J. Infrastruct. Syst., vol. 13, no. 2, pp. 157–167, 2007. N. Kazakis, I. Kougias, and T. Patsialis, “Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope-Evros region, Greece,” Sci. Total Environ., vol. 538, pp. 555–563, 2015. T. G. Schmitt, M. Thomas, and N. Ettrich, “Analysis and modeling of flooding in urban drainage systems,” J. Hydrol., vol. 299, no. 3–4, pp. 300–311, 2004. M. A. Benson, “Factors affecting the occurrence of floods in a humid region of diverse terrain,” U. S. Geol. Surv. Water Supply Pap., vol. 1580–B, pp. 1–64, 1962. J. Bendix and C. R. Hupp, “Hydrological and geomorphic impacts on riparian plant communities,” Hydrol. Process., vol. 14, no. October 1999, pp. 2977–2990, 2000. P. I. Korah and F. M. J. López, “Mapping Flood Vulnerable Areas in Quetzaltenango, Guatemala using GIS,” J. Environ. Earth Sci., vol. 5, no. 6, pp. 132–143, 2015. H. Zhang et al., “An integrated algorithm to evaluate flow direction and flow accumulation in flat regions of hydrologically corrected DEMs,” Catena, vol. 151, pp. 174–181, 2017. H. Schäuble, O. Marinoni, and M. Hinderer, “A GIS-based method to calculate flow accumulation by considering dams and their specific operation time,” Comput. Geosci., vol. 34, no. 6, pp. 635–646, 2008. M. A. Friedl et al., “Global land cover mapping from MODIS: algorithms and early results,” Remote Sens. Environ., vol. 83, pp. 287–302, 2002. S. N. Goward, B. Markham, D. G. Dye, W. Dulaney, and J. Yang, “Normalized difference vegetation index measurements from the advanced very high resolution radiometer,” Remote Sens. Environ., vol. 35, no. 2–3, pp. 257–277, 1991. J. Ma, G. Lin, J. Chen, and L. Yang, “An improved topographic wetness index considering topographic position,” 2010 18th Int. Conf. Geoinformatics, Geoinformatics 2010, no. 40401049, 2010. C. Z. Qin et al., “An approach to computing topographic wetness index based on maximum downslope gradient,” Precis. Agric., vol. 12, no. 1, pp. 32–43, 2011. A. Fariza, “Spatial Flood Risk Mapping in East Java , Indonesia , Using Analytic Hierarchy Process – Natural Breaks Classification,” pp. 405–410, 2017. M. S. Tehrany, B. Pradhan, and M. N. Jebur, “Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS,” J. Hydrol., vol. 504, pp. 69–79, 2013. G. Papaioannou, L. Vasiliades, and A. Loukas, “Multi-Criteria Analysis Framework for Potential Flood Prone Areas Mapping,” Water Resour. Manag., vol. 29, no. 2, pp. 399–418, 2015. J. P. Dasgupta, A., Grimaldi, S., Ramsankaran, R., & Walker, “Optimized glcm-based texture features for improved SAR-based flood mapping,” Geosci. Remote Sens. Symp., pp. 3258–3261, 2017. L. Li et al., “Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa,” Nat. Hazards, vol. 89, no. 3, p. 1495, 2017. I. Douglas, K. Alam, M. Maghenda, Y. Mcdonnell, L. Mclean, and J. Campbell, “Unjust waters: Climate change, flooding and the urban poor in Africa,” Environ. Urban., vol. 20, no. 1, pp. 187–205, 2008. V. Merwade, F. Olivera, M. Arabi, and S. Edleman, “Uncertainty in Flood Inundation Mapping: Current Issues and Future Directions,” J. Hydrol. Eng., vol. 13, no. 7, pp. 608–620, 2008. M. V Bernhofen et al., “A first collective validation of global fluvial flood models for major floods A first collective validation of global fluvial flood models for major floods in Nigeria and Mozambique,” 2018. R. G. Congalton, “A review of assessing the accuracy of classifications of remotely sensed data,” Remote Sens. Environ., vol. 37, no. 1, pp. 35–46, 1991. H. Bourenane and Y. Bouhadad, “GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine ( Northeast Algeria ),” 2014. I. A. Chandio and A. N. B. Matori, “GIS-based analytic hierarchy process as a multicriteria decision analysis instrument : a review,” no. Mendoza 1997, 2013. M. S. & J. H. Ivan Petiteville, Stephen Ward, George Dyke, “Satellite Earth Observations in support of disaster risk reduction, Special 2015 WCDRR Edition,” p. 84, 2015. K. Stein and R. Ebert, “Remote Sensing: Security Plus Defence,” SPIE Remote Sens. 8887, no. September, pp. 24–27, 2012. B. Petiteville, I., Ishida, C., Danzeglocke, J., Eddy, A., Gaetani, F., Frye, S., & Jones, “WCDRR and the CEOS activities on disaters,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 40, no. 7, p. 845, 2015. N. Kussul, A. Shelestov, and S. Skakun, “Use of Satellite and In-Situ Data to Improve Sustainability,” pp. 19–29, 2011. K. E. Joyce, S. E. Belliss, S. V. Samsonov, S. J. McNeill, and P. J. Glassey, “A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters,” Prog. Phys. Geogr., vol. 33, no. 2, pp. 183–207, 2009. A. Singh and K. K. Singh, “Satellite image classification using Genetic Algorithm trained radial basis function neural network, application to the detection of flooded areas,” J. Vis. Commun. Image Represent., vol. 42, pp. 173–181, 2017. D. E. Alexander et al., Assessment of Vulnerability to Natural Hazards. 2014. R. L. Ciurean and D. Schröter, “Conceptual Frameworks of Vulnerability Assessments for Natural Disasters Reduction,” Approaches to disaster Manag. Implic. hazards, emergencies disasters, 2013. S. Zahran, S. D. Brody, W. G. Peacock, A. Vedlitz, and H. Grover, “Social vulnerability and the natural and built environment: A model of flood casualties in Texas,” Disasters, vol. 32, no. 4, pp. 537–560, 2008. D. I. Behanzin, M. Thiel, J. Szarzynski, and M. Boko, “GIS-Based Mapping of Flood Vulnerability and Risk in the Bénin Niger River Valley,” Int. J. Geomatics Geosci., vol. 6, no. 3, pp. 1653–1669, 2016. J. Birkmann and H. Security, “MeasuringVulnerabilityto NaturalHazards: TowardsDisasterResilientSocietie,” J. Risk Insur., vol. 77, no. 4, pp. 959–961, 2010. U. C. Nkwunonwo, M. Whitworth, and B. Baily, “Review article: A review and critical analysis of the efforts towards urban flood risk management in the Lagos region of Nigeria,” Nat. Hazards Earth Syst. Sci., vol. 16, no. 2, pp. 349–369, 2016. P. Sayers, E. Penning-Rowsell, and M. Horritt, “Flood vulnerability, risk, and social disadvantage: currentand future patterns in the UK,” Reg. Environ. Chang., pp. 339–352, 2017. H. de Moel, J. van Alphen, and J. C. J. H. Aerts, “Flood maps in Europe – methods, availability and use,” Nat. Hazards Earth Syst. Sci., vol. 9, no. 2, pp. 289–301, 2009. [S. Purkis and V. Klemas, Remote Sensing and Global Environmental Change, 1st ed. John Wiley & Sons, 2011. M. B. Anderson, “Vulnerability to disaster and sustainable development: A general framework for assessing vulnerability,” Disaster Prevention for Sustainable Development. pp. 41–59, 1995. S. Informatics, “a Distributed Flood Inundation Model Integrating With Rainfall-Runoff Processes Using Gis and Remote Sensing Data,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., pp. 1513–1518, 2001. M. Rusdi, R. Roosli, and M. S. S. Ahamad, “Land evaluation suitability for settlement based on soil permeability, topography and geology ten years after tsunami in Banda Aceh, Indonesia,” Egypt. J. Remote Sens. Sp. Sci., vol. 18, no. 2, pp. 207–215, 2015. A. G. Ponette-González et al., “Managing water services in tropical regions: From land cover proxies to hydrologic fluxes,” Ambio, vol. 44, no. 5, pp. 367–375, 2015. Z. Zhu et al., “Remote Sensing of Environment Including land cover change in analysis of greenness trends using all available Landsat 5 , 7 , and 8 images : A case study from Guangzhou , China ( 2000 – 2014 ),” Remote Sens. Environ., 2016. H. Aksoy, V. S. O. Kirca, H. I. Burgan, and D. Kellecioglu, “Hydrological and hydraulic models for determination of flood-prone and flood inundation areas,” Proc. Int. Assoc. Hydrol. Sci., vol. 373, pp. 137–141, 2016. V. Rahimpour, Y. Zeng, C. M. Mannaerts, and Z. (Bob) Su, “Attributing seasonal variation of daily extreme precipitation events across The Netherlands,” Weather Clim. Extrem., vol. 14, no. November 2016, pp. 56–66, 2016. T. Dunne, “Stochastic aspects of the relations between climate, hydrology and landform evolution,” Trans. Japanese Geomorphol. Union, vol. 12, pp. 1–24, 1991. Z. W. Kundzewicz et al., “Summer floods in Central Europe - Climate change track?,” Nat. Hazards, vol. 36, no. 1–2, pp. 165–189, 2005. A. Spence, W. Poortinga, C. Butler, and N. F. Pidgeon, “Perceptions of climate change and willingness to save energy related to flood experience,” Nat. Clim. Chang., vol. 1, no. 1, pp. 46–49, 2011. M. A. Nayak and S. Ghosh, “Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier,” Theor. Appl. Climatol., vol. 114, no. 3–4, pp. 583–603, 2013. S. S. Pallant, J., & Manual, A step by step guide to data analysis using SPSS. Berkshire UK: McGraw-Hill Education., 2010. J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, “Multivariate Data Analysis,” Vectors. p. 816, 2010. A. Acock, C., “Working With Missing values,” J. Marriage Fam., vol. 67, no. November, pp. 1012–1028, 2005. J. M. Wooldridge, “Applications of Generalized Method of Moments Estimation,” J. Econ. Perspect., vol. 15, no. 4, pp. 87–100, 2001. Z. Wang, X. Huang, Y. Song, and J. Xiao, “An outlier detection algorithm based on the degree of sharpness and its applications on traffic big data preprocessing,” 2017 IEEE 2nd Int. Conf. Big Data Anal. ICBDA 2017, pp. 478–482, 2017. R. B. Kline, Principles and practice of structural equation modeling, 3rd ed., vol. 156. London, United Kingdom: The Guildford Press, 2011. S.-H. Lin and P.-J. Hsieh, Principles and Practice of Structural Equation Modeling, Third Edit., vol. 20, no. 1. New York, 2010. B. G. Tabachnick and L. S. Fidell, Using Multivariate Statistics 6th Ed. Pearson, 2013. A. Ghasemi and S. Zahediasl, “Normality tests for statistical analysis: A guide for non-statisticians,” Int. J. Endocrinol. Metab., vol. 10, no. 2, pp. 486–489, 2012. A. I. FLEISHMAN and STEVENS, “A Method for Simulating Non-Normal Distributions,” Psychometrika, vol. 43, no. 4, pp. 521–532, 1978. M. K. Cain, Z. Zhang, and K. H. Yuan, “Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation,” Behav. Res. Methods, vol. 49, no. 5, pp. 1716–1735, 2017. P. M. Dumas, A. Pérez, I. Rodríguez, C. Csr, and P. M. Dumas, “Earnings management in non-public companies: the case of for-profit hospice organizations,” J. Public Budgeting, Account. Financ. Manag., vol. 29(1), pp. 1–19, 2017. T. Raziei, J. Daryabari, I. Bordi, and L. S. Pereira, “Spatial patterns and temporal trends of precipitation in Iran,” Theor. Appl. Climatol., vol. 115, no. 3–4, pp. 531–540, 2014. Z. Şen, Innovative trend methodologies in science and engineering, 1st ed. Istanbul, Turkey: Springer International Publishing, 2017. R. C. Balling, M. S. Keikhosravi Kiany, S. Sen Roy, and J. Khoshhal, “Trends in Extreme Precipitation Indices in Iran: 1951-2007,” Adv. Meteorol., vol. 2016, 2016. Y. Y. Loo, L. Billa, and A. Singh, “Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia,” Geosci. Front., vol. 6, no. 6, pp. 817–823, 2015. S. Hänsel, A. Schucknecht, and J. Matschullat, “The Modified Rainfall Anomaly Index (mRAI)—is this an alternative to the Standardised Precipitation Index (SPI) in evaluating future extreme precipitation characteristics?,” Theor. Appl. Climatol., vol. 123, no. 3–4, pp. 827–844, 2016. A. Pauliková and O. Železník, “Multicriterial analysis of factors considering intensity and extent of floods,” Proc. 2014 15th Int. Carpathian Control Conf. ICCC 2014, pp. 418–423, 2014. M. Madruga De Brito, M. Evers, A. Delos, and S. Almoradie, “Participatory flood vulnerability assessment: a multi-criteria approach,” Hydrol. Earth Syst. Sci., vol. 22, pp. 373–390, 2018. H. B. Rasmussen et al., Multi-Criteria Decision Making in Maritime Studies and Logistics, vol. 260. CA, USA: Springer, 2018. S. Yahaya, N. Ahmad, and R. F. Abdalla, “Multicriteria Analysis for Flood Vulnerable Areas in Hadejia-Jama’are River Basin, Nigeria,” Eur. J. Sci. Res., vol. 42, no. 1, pp. 71–83, 2010. S. Liu, Q. Zhao, M. Wen, L. Deng, S. Dong, and C. Wang, “Assessing the impact of hydroelectric project construction on the ecological integrity of the Nuozhadu Nature Reserve, southwest China,” Stoch. Environ. Res. Risk Assess., vol. 27, no. 7, pp. 1709–1718, 2013. M. L. Ojigi, F. I. Abdulkadir, and M. O. Aderoju, “Geospatial Mapping and Analysis of the 2012 Flood Disaster in Central Parts of Nigeria,” 8th Natl. GIS Symp., pp. 1–14, 2013. A. Jahan and K. L. Edwards, “A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design,” Mater. Des., vol. 65, pp. 335–342, 2015. L. M. Camarinha-Matos, A. J. Falcão, N. Vafaei, and S. Najdi, “Normalization Techniques for Multi-Criteria Decision Making: Analytical Hierarchy Process Case Study,” IFIP Adv. Inf. Commun. Technol., vol. 470, no. October 2017, 2016. K. Teknomo, “Analytic hierarchy process (ahp) tutorial,” Revoledu. com, vol. 6, no. 4, pp. 1–20, 2006. R. G. Congalton, “Accuracy assessment and validation of remotely sensed and other spatial information,” Int. J. Wildl. Fire, vol. 10, no. 4, pp. 321–328, 2001. R. A. Mead, “Landsat Classification Accuracy Assessment Procedures : An Account of a National Working Conference,” 1981. D. Butler, “The web-wide world,” Nat. Publ. Gr., vol. 439, no. 7078, pp. 776–778, 2006. D. Potere, “Horizontal positional accuracy of google earth’s high-resolution imagery archive,” Sensors, vol. 8, no. 12, pp. 7973–7981, 2008. P. Heck and A. Zaidman, “A framework for quality assessment of just-in-time requirements: the case of open source feature requests,” Requir. Eng., vol. 22, no. 4, pp. 453–473, 2017. B. M. Pulka, R. Rikwentishe, U. A. U. Mani, and M. M. Jossiah, “Variation of Attitude among University Students towards Entrepreneurship Education,” J. Bus. Adm. Educ., vol. 7, no. 2, pp. 177–195, 2015. W. N. Adger, “Vulnerability,” Glob. Environ. Chang., vol. 16, no. 3, pp. 268–281, 2006. R. Chambers, “Vulnerability, coping and policy (editorial introduction),” IDS Bull., vol. 37, no. 4, pp. 33–40, 2006. S. Hales, “Climate Change 2007: Impacts, Adaptation and Vulnerability,” Intergov. Panel Clim. Chang., no. January, 2016. M. Pelling, “What Determines Vulnerability To Floods; A Case Study In Georgetown, Guyana,” Environ. Urban., vol. 9, no. 1, pp. 203–226, 1997. A. Zerger and S. Wealands, “Beyond modelling: Linking models with GIS for flood risk management,” Nat. Hazards, vol. 33, no. 2, pp. 191–208, 2004. A. Hooijer, F. Klijn, G. Bas, M. Pedroli, and A. D. G. V. A. N. Os, “Towards sustainable flood risk management in the rhine and meuse river basins : synopsis of the findings of irma-sponge,” vol. 357, no. May 2003, pp. 343–357, 2004. A. A. Akinsanola and K. O. Ogunjobi, “Recent homogeneity analysis and long-term spatio-temporal rainfall trends in Nigeria,” Theor. Appl. Climatol., vol. 128, no. 1–2, pp. 275–289, 2017. D. Roberts and N. Khattri, Designing a Results Framework for Achieving Results: a How-To Guide, vol. 26, no. 1. 2014. M. Alavi, M. Archibald, R. McMaster, V. Lopez, and M. Cleary, “Aligning theory and methodology in mixed methods research: Before Design Theoretical Placement,” Int. J. Soc. Res. Methodol., vol. 5579, pp. 1–14, 2018. A. A. Komolafe, S. A. Adegboyega, and F. O. Akinluyi, “A Review of Flood Risk Analysis in Nigeria A Review of Flood Risk Analysis in Nigeria,” no. September, 2015. T. Collins, “The political ecology of hazard vulnerability: Marginalization, facilitation and the production of differential risk to urban wildfires in Arizona’s White Mountains,” J. Polit. Ecol., vol. 15, pp. 21–43, 2008. S. L. Cutter, B. J. Boruff, and W. L. Shirley, “Social vulnerability to environmental hazards,” Soc. Sci. Q., vol. 84, no. 2, pp. 242–261, 2003. B. Wisner and P. Walker, “The world conference on disaster viewed through the lens of political ecology: A dozen big questions for Kobe and beyond,” Capital. Nature, Social., vol. 16, no. 2, pp. 89–95, 2005. S. Hallegatte, A. Vogt-Schilb, M. Bangalore, and J. Rozenberg, “Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters,” 2016. UNISDR, “Sendai Framework for Disaster Risk Reduction 2015 - 2030,” Third World Conference on Disaster Risk Reduction, Sendai, Japan, 14-18 March 2015., 2015. |