Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut

Kajian ini bertujuan untuk mengenal pasti kehadiran telatah kalut dan meramal data sirimasa aras air sungai di kawasan yang berkepentingan di Malaysia denganmenggunakan pendekatan kalut. Kajian ini merangkumi tiga objektif utama iaitu mengenalpasti kehadiran telatah kalut pada aras air sungai, meram...

Full description

Saved in:

Bibliographic Details
Main Author:	Adib Mashuri
Format:	thesis
Language:	zsm
Published:	2020
Subjects:
Online Access:	https://ir.upsi.edu.my/detailsg.php?det=6746
Tags:	Add Tag No Tags, Be the first to tag this record!

id	oai:ir.upsi.edu.my:6746
record_format	uketd_dc
institution	Universiti Pendidikan Sultan Idris
collection	UPSI Digital Repository
language	zsm
topic
spellingShingle	Adib Mashuri Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut
description	Kajian ini bertujuan untuk mengenal pasti kehadiran telatah kalut dan meramal data sirimasa aras air sungai di kawasan yang berkepentingan di Malaysia denganmenggunakan pendekatan kalut. Kajian ini merangkumi tiga objektif utama iaitu mengenalpasti kehadiran telatah kalut pada aras air sungai, meramal aras air sungai menggunakan pendekatankalut dan menambahbaik kaedah peramalan purata setempat (kpps) untuk peramalan aras air sungaidi kawasan berkepentingan di Malaysia. Kawasan berkepentingan di Malaysia diperincikan kepadadua kawasan iaitu kawasan tadahan sungai yang berbeza ketinggian tanah dan kawasan dataran banjir.Tiga batang sungai telah dipilih untuk memenuhi objektif kajian iaitu kawasan tadahan sungai yangberbeza ketinggian tanah adalah di Sungai Pahang yang melibatkan kawasan tanah rendah(skala masa jam) dan tanah tinggi (skala masa harian). Manakala kawasan dataran banjiradalah di Sungai Kelantan (skala masa jam) dan Sungai Dungun (skala masa jam). Dapatan kajianbagi objektif pertama membuktikan telatah kalut hadir terhadap data siri masa aras airsungai yang dikaji menggunakan kaedah Cao menunjukkan sekurang-kurangnya satu nilai E2(d) 1manakala kaedah plot ruang fasa pula menunjukkan wujud rantau penarik pada ruang fasa. Hasildapatan objektif kedua menunjukkan data siri masa aras sungai memberikan peramalan yang sangatcemerlang (pekali korelasi, CC >0.99) menggunakan kombinasi kaedah kpps dan d songsangberbanding kaedah lain dalam kajian ini. Kaedah penambahbaikan kpps dapatmemberikan peramalan yang lebih baik berbanding kaedah kpps kerana kaedah penambahbaikankpps dapat memberikan nilai pekali korelasi yang lebih tinggi. Kesimpulannya, pendekatankalut berjaya meramal siri masa aras air sungai di kawasan berkepentingan di Malaysia. Implikasikajian ini dapat menyumbangkan maklumat aras sungai kepada pihak yang berkenaan bagipengurusan sumber air dan pengawalanbanjir.
format	thesis
qualification_name
qualification_level	Master's degree
author	Adib Mashuri
author_facet	Adib Mashuri
author_sort	Adib Mashuri
title	Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut
title_short	Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut
title_full	Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut
title_fullStr	Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut
title_full_unstemmed	Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut
title_sort	peramalan data siri masa aras air sungai di kawasan berkepentingan malaysia menggunakan pendekatan kalut
granting_institution	Universiti Pendidikan Sultan Idris
granting_department	Fakulti Sains dan Matematik
publishDate	2020
url	https://ir.upsi.edu.my/detailsg.php?det=6746
_version_	1747833303859200000
spelling	oai:ir.upsi.edu.my:67462022-02-15 Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut 2020 Adib Mashuri Kajian ini bertujuan untuk mengenal pasti kehadiran telatah kalut dan meramal data sirimasa aras air sungai di kawasan yang berkepentingan di Malaysia denganmenggunakan pendekatan kalut. Kajian ini merangkumi tiga objektif utama iaitu mengenalpasti kehadiran telatah kalut pada aras air sungai, meramal aras air sungai menggunakan pendekatankalut dan menambahbaik kaedah peramalan purata setempat (kpps) untuk peramalan aras air sungaidi kawasan berkepentingan di Malaysia. Kawasan berkepentingan di Malaysia diperincikan kepadadua kawasan iaitu kawasan tadahan sungai yang berbeza ketinggian tanah dan kawasan dataran banjir.Tiga batang sungai telah dipilih untuk memenuhi objektif kajian iaitu kawasan tadahan sungai yangberbeza ketinggian tanah adalah di Sungai Pahang yang melibatkan kawasan tanah rendah(skala masa jam) dan tanah tinggi (skala masa harian). Manakala kawasan dataran banjiradalah di Sungai Kelantan (skala masa jam) dan Sungai Dungun (skala masa jam). Dapatan kajianbagi objektif pertama membuktikan telatah kalut hadir terhadap data siri masa aras airsungai yang dikaji menggunakan kaedah Cao menunjukkan sekurang-kurangnya satu nilai E2(d) 1manakala kaedah plot ruang fasa pula menunjukkan wujud rantau penarik pada ruang fasa. Hasildapatan objektif kedua menunjukkan data siri masa aras sungai memberikan peramalan yang sangatcemerlang (pekali korelasi, CC >0.99) menggunakan kombinasi kaedah kpps dan d songsangberbanding kaedah lain dalam kajian ini. Kaedah penambahbaikan kpps dapatmemberikan peramalan yang lebih baik berbanding kaedah kpps kerana kaedah penambahbaikankpps dapat memberikan nilai pekali korelasi yang lebih tinggi. Kesimpulannya, pendekatankalut berjaya meramal siri masa aras air sungai di kawasan berkepentingan di Malaysia. Implikasikajian ini dapat menyumbangkan maklumat aras sungai kepada pihak yang berkenaan bagipengurusan sumber air dan pengawalanbanjir. 2020 thesis https://ir.upsi.edu.my/detailsg.php?det=6746 https://ir.upsi.edu.my/detailsg.php?det=6746 text zsm closedAccess Masters Universiti Pendidikan Sultan Idris Fakulti Sains dan Matematik Abarbanel, H. D. I. (1996). Analysis of Observed Chaotic Data. New York, NY:Springer New York.Acharya, R., Pal, J., Das, D., & Chaudhuri, S. (2019). Long?Range Forecast of Indian Summer Monsoon Rainfall Using an Artificial Neural Network Model. MeteorologicalApplications, 26(3), met.1766.Adenan, N. H., & Noorani, M. S. M. (2013). River Flow Prediction Using Nonlinear Prediction Method.International Journal of Physical and Mathematical Sciences, 7(11).Adenan, N. H. (2015). Analisis Dan Peramalan Data Siri Masa Aliran Sungai Dengan MenggunakanPendekatan Kalut. Universiti Kebangsaan Malaysia (UKM).Adenan, N. H., & Noorani, M. S. M. (2016). Multiple Time-Scales Nonlinear Prediction ofRiver Flow Using Chaos Approach. Jurnal Teknologi, 78(7).Adenan, N. H., & Noorani, M. S. M. (2015a). Peramalan Data Siri Masa Aliran sungai di DataranBanjir dengan Menggunakan Pendekatan Kalut. Sains Malaysiana, 44(3), 463471.Aeschbacher, J., Liniger, H., & Weingartner, R. (2009). River Water Shortage in aHighlandLowland System. Environmental Sciences.Agensi Pengurusan Bencana Negara (NADMA). (2018). Portal Bencana. Retrieved December 10,2018.Ahmad, A. K., Mushrifah, I., & Othman, M. S. (2009). Water Quality and Heavy Metal Concentrationsin Sediment of. Sains Malaysiana (Vol. 38).Akhir, N. M., Azman, A., Hassan, N., & Akhir, N. H. M. (2017). Kajian Penelitian Masalah MangsaBencana Banjir Disember 2014 Di Kelantan. Journal of Social Sciences & Humanities.Albostan, A., & nz, B. (2015). Implementation of Chaotic Analysis on River DischargeTime Series. Energy and Power Engineering, 7, 8192.Albostan, A., & nz, B. (2015). Implementation of Chaotic Analysis on River DischargeTime Series. Energy and Power Engineering, 07(03), 8192.Ambak, M. A., & Zakaria, M. Z. (2010). Freshwater Fish Diversity in Sungai Kelantan.Universiti Malaysia Terengganu (UMT).Ghani, A. A., Chang, C. K., Pua, H. T. & Ismail, W. R. (2016). Flood inundationanalysis for large scale river using hydrodynamic and sediment transport model Case study of Sungai Pahangs December 2014 flood. Persidangan Kajian Bencana Banjir 2014, 9.Anika, N., & Kato, T. (2019). Modeling River Flow using Artificial Neural Networks: A Case Study onSumani Watershed. Pertanika J. Sci. & Technol, 27(S1), 179 188.Arbain, S. H. (2014). Non-Linear Water Level Forecasting of Dungun River UsingHybridization of Backpropagation Neural Network and Genetic Algorithm .Arbain, S. H., & Wibowo, A. (2012). Time series methods for water level forecasting of Dungun riverin Terengganu Malaysia, 9.Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar timeseries. Physica D: Nonlinear Phenomena, 110(12), 4350.Chapman, D. (1992). Water Quality Assessments-A Guide to Use of Biota, Sediments and Water inEnvironmental Monitoring-Second Edition. London: E & FN Spon.Cheng, H., & Sandu, A. (2009). Uncertainty quantification and apportionment in air quality modelsusing the polynomial chaos method. Environmental Modelling & Software, 24(8), 917925.Chennai, T., Padma, K., Selvaraj, R. S., & Boaz, B. M. (2013). Use of Chaotic and Time SeriesAnalysis on Surface Ozone Study at the Tropical Region. Universal Journal of Environmental Researchand Technology, 3(6), 650659.Ching, Y., Baharudin, Y., Ekhwan, T. M., Lee, Y., Maimon, A., & Salmijah, S. (2013). Impact ofclimate change on flood risk in the Muar River Basin of Malaysia. In Disaster Advances 6 (pp.1117). Universiti Kebangsaan Malaysia, Selangor.Dewan Bahasa dan Pustaka. (2005). Kamus Dewan (Edisi 4.). Kuala Lumpur: Dewan Bahasa dan PustakaDucard, G. (2017). Modeling and Analysis of Dynamic Systems Institute for Dynamic Systems andControl. Zurich, Switzerland.Elshorbagy, A., Simonovic, S. P., & Panu, U. S. (2002). Estimation of missingstreamflow data using principles of chaos theory. Journal of Hydrology, 255(1 4), 123133.Fathima, T. A., & Jothiprakash, V. (2014). Behavioural analysis of a time series-Achaotic approach. S Adhan A, 39, 659676.Frison, T. W., I Abarbanel, H. D., Earle, M. D., Schultz, J. R., Scherer, W. D., & Diego,S. (1999). Chaos and predictability in ocean water levels. Journal Of Geophysical Research,104(C4).Gasim, M. B., Toriman, M. I., & Idris, M. (2013). River flow conditions and dynamic state analysisof Pahang river.Gasim, M. B., Adam, J., Toriman, M. E., Rahim, S. A., & Juahir, H. (2007). Coastal FloodPhenomenon in Terengganu, Malaysia: Special Reference to Dungun. Reseach Journal ofEnviromental Science, 3, 9.Ghani, A. A., Chang, C. K., Leow, C. S., & Zakaria, N. A. (2012). International Journal of RiverBasin Management Sungai Pahang digital flood mapping: 2007 flood, 11.Ghorbani, M. A., Daneshfaraz, R., Arvanagi, H., Pourzangbar, A., Saghebian, S. M., & Kar, K. K.(2012). Local Prediction in River Discharge Time Series. Journal of Civil Engineering and Urbanism,2(2), 5155.Ghosh, S., & Mistri, B. (2015). Geographic Concerns on Flood Climate and FloodHydrology in Monsoon-Dominated Damodar River Basin, Eastern India. Geography Journal,2015, 116.Gurjar, M., Naik, P., Mujumdar, G., & Vaidya, T. (2018). Stock Market Prediction Using ANN.International Research Journal of Engineering and Technology, 5(3), 27582761.Hamid, N. Z. A. & Noorani, M. S. M. (2013). An Improved Prediction Model of Ozone ConcentrationTime Series Based On Chaotic Approach. Int. J. Math. Comput. Sci. Eng.Hamid, N. Z. A. & Noorani, M. S. M. (2014). Suatu Kajian Perintis Menggunakan Pendekatan Kalutbagi Pengesanan Sifat dan Peramalan Siri Masa Kepekatan PM10. Sains Malaysiana, 43(3),475481.Hamid, N. Z. A. (2015). Pemodelan siri masa kepekatan bahan pencemar udara 03,PM10 danjerebu menerusi pendekatan kalut. Universiti Kebangsaan Malaysia (UKM).Hamid, N. Z. A., (2018). Application of chaotic approach in forecasting highlands temperaturetime series. IOP Conf. Ser.: Earth Environ. Sci, 169. 7Hamid, N. Z. A. (2015). Pemodelan Siri Masa Kepekatan Bahan Pencemar Udara 03, Pmlo Dan JerebuMenerusi Pendekatan Kalut. Tesis doktor falsafah, Universiti Kebangsaan Malaysia.Hamid, N. Z. A. & Noorani, M. S. M. (2017). Aplikasi Model Baharu Penambahbaikan Pendekatan Kalut ke atas Peramalan Siri Masa Kepekatan Ozon. Sains Malaysiana, 46(8), 13331339.Ibrahim, H. A. & Ahmad, M. K. F. (2017). Geografi Tingkatan 1. Era Visi Sdn Bhd.Hohle, S. M., & Teigen, K. H. (2015). Forecasting forecasts: The trend effect. Judgment andDecision Making, 10(5), 416428.Hussain, T. P. R. S., & Ismail, H. (2011). Land Use Changes Analysis for KelantanBasin Using Spatial Matrix Technique "Patch Analyst" in Relation to Flood Disaster.Journal of Techno-Social, 3(1).Ibrahim, F. (2016). Strategi Penambahbaikan Pengurusan Bencana Banjir Besar Di Kelantan. UniversitiTeknologi Malaysia.Jabatan Meteorologi Malaysia. (2017). Laporan Tahunan 2016.Jabatan Pengairan dan Saliran Malysia (JPS). (2015). Kompedium Data dan Maklumat Asas.Jabatan Pengairan dan Saliran Selangor (JPS). (2018). Pengurusan Banjir.Jabatan Perangkaan Malaysia (JPM). (2018). Department of Statistics Malaysia OfficialPortal.Jayawardena, A. W. (1997). Runoff forecasting using a local approximation method.IAHS Publ.Jieni, X. & Zhongke, S. (2008). Short-Time Traffic Flow Prediction Based on Chaos Time Series Theory. Journal Of Transportation Systems Engineering And Information Technology, 8(5),6872.Kantz, H. & Schreiber, T. (2004). Nonlinear Time Series Analysis.Kaplan, D. & Glass, L. (1995). Understanding Nonlinear Dynamics. Texts in Applied Mathematics.,(jil. 19).Karri, R. R., Badwe, A., Wang, X., Serafy, G. E., Sumihar, J., Babovic, V. & Gerritsen,H. (2013). Application of data assimilation for improving forecast of water levels and residualcurrents in Singapore regional waters. Ocean Dynamics, 63(1), 43 61.Khairul, M., Kamarudin, A., Toriman, E., Sulaiman, N. H., Ata, F. M., Gasim, M. B., Aziz, A. (2015). Klasifikasi Sungai Tropika Menggunakan Teknik Kemometrik:Kajian Kes Di Sungai Pahang, Malaysia. Malaysian Journal of Analytical Sciences, 19,10011018.Khatami, S. (2013). Nonlinear Chaotic and Trend Analyses of Water Level at Urmia Lake, Iran DoesClimate Variability Explain Urmia Lake Depletion. Lund University.Khatibi, R., Ghorbani, M. A., Aalami, M. T., Kocak, K., Makarynskyy, O.,Makarynska, D., & Aalinezhad, M. (2011). Dynamics of hourly sea level at Hillarys BoatHarbour, Western Australia: a chaos theory perspective. Ocean Dynamics, 61(11), 17971807.Khatibi, R., Sivakumar, B., Ghorbani, M. A., Kisi, O., Koak, K., & Zadeh, D. F.(2012). Investigating chaos in river stage and discharge time series. Journal ofHydrology, 414415, 108117.Khokhlov, V., Glushkov, A. A., Loboda, A. N., Serbov, A. N., & Zhurbenko, A. K. (2008). Signaturesof low-dimensional chaos in hourly water level measurements at coastal site of Mariupol, Ukraine.Lak, R., Darvishikhatuoni, J., & Mohammadi, A. (2012). Study Of Paleolimnology And Causes Of SuddenDecrease Of Urmia Lake Water Table. Journal of Geotechnical Gology (Applied Geology), 7(4),343358.Li, T.-Y., & Yorke, J. A. (1975). Period Three Implies Chaos. The American MathematicalMonthly (Vol. 82).Li, Y., Liu, Y., & Zhu, H. (2017). Analysis of the chaotic characteristics of traffic flow undercongested traffic condition. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp.49494954). IEEE.Lorenz, E. N. (1963). Deterministic Nonperiodic Flow. Journal Of The Atmospheric Sciences, 20, 12.Lun, P. I., Gasim, M. B., Toriman, M. E., Rahim, S. A., & Kamaruddin, K. A. (2011). HydrologicalPattern Of Pahang River Basin And Their Relation To Flood Historical Event (CorakHidrologi Lembangan Sungai Pahang dan Hubungannya dengan, 6, 9.Mahmoudabadi, A., & Andalibi, S. (2014). The Assessment of Applying Chaos Theory for Daily TrafficEstimation. Industrial Engineering and Operations Management, 559567.Marcinkowski, P., Grygoruk, M., Marcinkowski, P., & Grygoruk, M. (2017). Long- Term DownstreamEffects of a Dam on a Lowland River Flow Regime: Case Study of the Upper Narew. Water,9(10), 783.Martin, C., Gill, J., Cacic, I., Muchemi, S., & Rubiera, J. (2007). World MeteorologicalOrganization 2007.Martins, O. Y., Sadeeq, M. A., & Ahaneku, I. E. (2011). Nonlinear Deterministic Chaos in BenueRiver Flow Daily Time Sequence. Journal of Water Resource and Protection, 03(10), 747757.Matlab. (2009). Matlab and Statistics Toolbox Release 2009b. The MathWorks, Inc., Natick,Massachusetts, United States.Merkwirth, C., Parlitz, U., Wedekind, I., Engster, D., & Lauterborn, W. (2009).TSTOOL and User Manual.Mesbahzadeh, M. (2016). Applying the 0-1 test on the analysis of climate and weather data usingchaos theory. Journal of Fundamental and Applied Sciences, 8(2), 1188.Mihailovi?, D. T., Nikoli?-?ori?, E., Arseni?, I., Malinovi?-Mili?evi?, S., Singh, V. P., Stoi?,T. & Stoi?, B. (2018). Analysis of Daily Streamflow Complexity by Kolmogorov Measuresand Lyapunov Exponent.Musa, S. M. S.,] & Shafii, H. (2012). Pengurusan sistem saliran dalam menangani masalahbanjir di Batu Pahat, Johor (pp. 125146). UTHM, Batu Pahat.Ooi, S. H., Samah, A. A. & Braesicke, P. (2013). Primary productivity and itsvariability in the equatorial South China Sea during the northeast monsoon. AtmosphericChemistry and Physics Discussions, 13(8), 2157321608.Ooi, See Hai, Samah, A. A. & Braesicke, P. (2011). A case study of the Borneo Vortex genesis andits interactions with the global circulation. Journal of Geophysical Research: Atmospheres,116(D21).Paimin, S. & Pramono, I. B. (2009). Teknik mitigasi banjir dan tanah longsor.Tropenbos International Indonesia Programme, Indonesia. Indonesia.Pandey, A. & Srinivas, V. V. (2015). Use of Data Driven Techniques for Short Lead Time StreamflowForecasting in Mahanadi Basin. Aquatic Procedia, 4, 972978.Peitgen, H.-O., Ju?rgens, H., & Saupe, D. (2004). Chaos and fractals : new frontiers of science.Springer.Peng, D., Xin, F., Zhang, L., Gao, Z., Zhang, W., Wang, Y., Chen, X. & Wang, Y. (2017). Nonlineartime-series analysis of optical signals to identify multiphase flow behavior inmicrochannels. AIChE Journal, 63(6), 23782385.Pomeroy, J. W., Stewart, R. E., & Whitfield, P. H. (2016). The 2013 flood event in the SouthSaskatchewan and Elk River basins: Causes, assessment and damages. Canadian Water Resources Journal / Revue Canadienne Des Ressources Hydriques, 41(12), 105117.Rahman, H. A. (2009). Suatu tinjauan terhadap permasalahan banjir kilat di Lembah Klang. InINasir Nayan et a. (eds) Persekitaran fizikal di Malaysia: Isu dan cabaran semasa (pp.910). Universiti Pendidikan Sultan Idris, Tanjong Malim.Ratner, B. (2009). The correlation coefficient: Its values range between 1/1, or do they.Journal of Targeting, Measurement and Analysis for Marketing, 17(2), 139142.Razali, A., Ismail, S. N. S., Awang, S., Praveena, S. M. & Abidin, E. Z. (2018). Land use change inhighland area and its impact on river water quality: a review of case studies in Malaysia.Ecological Processes, 7(1), 19.Regonda, S. K., Rajagopalan, B., Lall, U., Clark, M. & Moon, Y.-I. (2005). Localpolynomial method for ensemble forecast of time series. Nonlinear Processes in Geophysics (Vol.12).Rodriguez-Iturbe, I., Febres De Power, B., Sharifi, M. B., & Georgakakos, K. P. (1989).Chaos in rainfall. Water Resources Research, 25(7), 16671675.Ruslan, A. B., & Hamid, N. Z. A. (2019). Application of Improved Chaotic Method in DeterminingNumber of k-Nearest Neighbor for CO Data Series. International Journal of Engineering andAdvanced Technology (IJEAT), 8(6S3), 1014.Sapini, M. L., Adam, N. S., Ibrahim, N., Rosmen, N., & Yusof, N. M. (2017). The presence of chaosin rainfall by using 0-1 test and correlation dimension. In AIP Conference Proceedings (Vol. 1905,p. 050040). AIP Publishing LLC.Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation Coefficients. Anesthesia & Analgesia,126(5), 17631768.Shang, P., & Kamae, S. (2005). Fractal nature of time series in the sediment transport phenomenon.Chaos, Solitons & Fractals, 26(3), 9971007.Shang, P., Na, X., & Kamae, S. (2009). Chaotic analysis of time series in the sediment transportphenomenon. Chaos, Solitons & Fractals, 41(1), 368379.Sharifi, M. B., Georgakakos, K. P., Rodriguez-Iturbe, I., Sharifi, M. B., Georgakakos,K. P., & Rodriguez-Iturbe, I. (1990). Evidence of Deterministic Chaos in the Pulse of StormRainfall. Journal of the Atmospheric Sciences, 47(7), 888893.Siek, M. (2011). Predicting storm surges : chaos, computational intelligence, dataassimilation, ensembles. Delft University of Technology. Netherlands.: CRC Press.Sivakumar, B. (2002). A phase-space reconstruction approach to prediction of suspendedsediment concentration in rivers. Journal of Hydrology, 258(14), 149 162.Sivakumar, B. (2001). Rainfall dynamics at different temporal scales: A chaoticperspective. Hydrology and Earth System Sciences, 5(4), 645651.Sivakumar, B., & Berndtsson, R. (2010). Advances in Data-Based Approaches for HydrologicModeling and Forecasting. World Scientific.Sivakumar, B., Berndtsson, R., Olsson, J., & Jinno, K. (2001). Evidence of chaos in therainfall-runoff process. Hydrological Sciences Journal, 46(1), 131145.Sivakumar, B., & Jayawardena, A. W. (2002). An investigation of the presence of low- dimensional chaotic behaviour in the sediment transport phenomenon. Hydrological SciencesJournal, 47(3), 405416.Sivakumar, B., Sorooshian, S., Gupta, H. V., & Gao, X. (2001). A chaotic approach to rainfalldisaggregation. Water Resources Research, 37(1), 6172.Sivakumar, B. & Wallender, W. W. (2005). Predictability of river flow and suspendedsediment transport in the Mississippi River basin: a non-linear deterministic approach.Earth Surface Processes and Landforms, 30(6), 665677.Soeharn, A. W. H., Farid, M., Abidah, D. S., Maitsa, T. R., Setianingsih, & Majidah,N. (2019). Effect of extreme rain and land covering change in Jatihandap on 20 March 2018 flashflood. MATEC Web of Conferences, 270, 04003.Solomatine, D. P., Rojas, C. J., Velickov, S., & Wst, J. C. (2000). Chaos theory in predictingsurge water levels in the North Sea. 4-Th International Conference on Hydroinformatics, 8.Sprott, J. C. (2003). Chaos and Time-Series Analysis. Oxford University Press.Srivastava, P. K., Islam, T., Singh, S. K., Petropoulos, G. P., Gupta, M., & Dai, Q. (2016).Forecasting Arabian Sea Level Rise using Exponential Smoothing State Space Models and ARIMA fromTOPEX and Jason satellite Radar Altimeter Data. Meteorological Applications, 23(4), 633639.Su, L. (2010). Prediction of multivariate chaotic time series with local polynomialfitting. Computers & Mathematics with Applications, 59(2), 737744.Sulaiman, N. H., Khairul, M., Kamarudin, A., Toriman, E., Juahir, H., Marcus Ata, F., Sideng, U.(2017). Relationship of Rainfall Distribution and Water Level on Major Flood 2014 in Pahang RiverBasin, Malaysia.Suratman, S. (2013). Distribution of Total Petrogenic Hydrocarbon in Dungun River Basin, Malaysia :Oriental Journal of Chemistry. Oriental Journal of Chemistry, 29(1).Takens, F. (1981). Detecting strange attractors in turbulence. Dynamical Systems and Turbulence,Warwick.Tanty, R., & Desmukh, T. S. (2015). Application of Artificial Neural Network inHydrology- A Review. International Journal of Engineering Research & Technology,4(6).Tare, V., Gurjar, S. K., Mohanta, H., Kapoor, V., Modi, A., Mathur, R. P., & Sinha, R. (2017).Eco-geomorphological approach for environmental flows assessment in monsoon-driven highland rivers:A case study of Upper Ganga, India. Journal of Hydrology: Regional Studies, 13, 110121.Tongal, H., & Berndtsson, R. (2014). Phase-space reconstruction and self-excitingthreshold models in lake level forecasting: a case study of the three largest lakes of Sweden.Stochastic Environmental Research and Risk Assessment; 28(4), Pp 955-971 (2014), 28(4), 955971.Toriman, M. E., Juahir, H., Mokhtar, M., Gazim, M. B., Mastura, S., Abdullah, S., & Jaafar, O.(2009). Predicting for Discharge Characteristics in Langat River, Malaysia Using NeuralNetwork Application Model. Research Journal of Earth Sciences, 1(1), 1521.Velickov, S. (2004). Nonlinear dynamics and chaos with applications to tohydrodynamics and hydrological modelling. A.A. Balkema.Wang, X., & Babovic, V. (2014). Enhancing water level prediction through model residualcorrection based on Chaos theory and Kriging. International Journal for Numerical Methods inFluids, 75(1), 4262.Weng, T. K., & Mokhtar, M. (2010). Towards Integrated Water ResourcesManagement Approach in Malaysia: A Case Study in Pahang River Basin. Environment andNatural Resources J, 8(2), 4758.Yatim, B., Abdullah, M., & Surif, S. (2012). Banjir: Bencana alam. In Baharudin Yatim et al.(eds) Banjir besar Johor (pp. 1318). Universiti Kebangsaan Malaysia, Selangor.Yildirim, H. A., Hacinliyan, A. S., Akkaya, E. E., & Ikiel, C. (2016). Chaos in Time Series ofSakarya River Daily Flow Rate. Journal of Applied Mathematics and Physics, 04(10), 18491858.https://doi.org/10.4236/jamp.2016.410187Zaim, W. N. A. W. M. (2018). Peramalan Siri Masa Ozon Mengikut Monsun Di KawasanPendidikan Tinggi Malaysia Melalui Pendekatan Kalut. UPSI.Zaim, W. N. A. W. M., & Hamid, N. Z. A. (2017). Peramalan Bahan Pencemar Ozon (O3 ) diUniversiti Pendidikan Sultan Idris, Tanjung Malim, Perak, Malaysia Mengikut Monsun denganMenggunakan Pendekatan Kalut. Sains Malaysiana,46(12), 25232528.

Peramalan data siri masa aras air sungai di kawasan berkepentingan Malaysia menggunakan pendekatan kalut

Similar Items