Forecasting model for extreme rainfall using artificial neural network

Forecasting model for extreme rainfall using artificial neural network

Successive days of rainfall are known to cause flood. The forecasting of daily rainfall helps to estimate the occurrences of rainfall and number of wet days, while with a maximum of five consecutive days of rainfall, the magnitude of rainfall within a specified period can predict what may signify r...

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Main Author: Al-Qurayshi, Yasir Hilal Hadi
Format: Thesis
Language:eng
eng
Published: 2015
Subjects:
QA71-90 Instruments and machines
Online Access:https://etd.uum.edu.my/5282/1/s815184.pdf
https://etd.uum.edu.my/5282/2/s815184_abstract.pdf
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id my-uum-etd.5282
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Ku Mahamud, Ku Ruhana
Wan Ishak, Wan Hussain
topic QA71-90 Instruments and machines
spellingShingle QA71-90 Instruments and machines
Al-Qurayshi, Yasir Hilal Hadi
Forecasting model for extreme rainfall using artificial neural network
description Successive days of rainfall are known to cause flood. The forecasting of daily rainfall helps to estimate the occurrences of rainfall and number of wet days, while with a maximum of five consecutive days of rainfall, the magnitude of rainfall within a specified period can predict what may signify rainfall extremes. In this study, data mining and back propagation neural network (BPNN) have been established in developing the extreme rainfall forecasting models. Four forecasting models were developed to forecast the maximum five consecutive days of rainfall amount (PX5D) of the next month. The models only use the extreme rainfall indices outlined by STARDEX as predictors in forecasting. The first developed model uses six extreme rainfall indices in forecasting, the second model uses the values of the PX5D index of a three-month delay, the third model uses the previous six-month PX5D values, while the fourth model was developed to forecast the PX5D using the values of the same index of a twelve-month delay. It was found that when using the six extreme rainfall core indices, the forecasting error was the lowest. A regression model has been developed using the six extreme rainfall indices to compare the performance measurements with the BPNN model that uses the same indices
format Thesis
qualification_name masters
qualification_level Master's degree
author Al-Qurayshi, Yasir Hilal Hadi
author_facet Al-Qurayshi, Yasir Hilal Hadi
author_sort Al-Qurayshi, Yasir Hilal Hadi
title Forecasting model for extreme rainfall using artificial neural network
title_short Forecasting model for extreme rainfall using artificial neural network
title_full Forecasting model for extreme rainfall using artificial neural network
title_fullStr Forecasting model for extreme rainfall using artificial neural network
title_full_unstemmed Forecasting model for extreme rainfall using artificial neural network
title_sort forecasting model for extreme rainfall using artificial neural network
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2015
url https://etd.uum.edu.my/5282/1/s815184.pdf
https://etd.uum.edu.my/5282/2/s815184_abstract.pdf
_version_ 1747827899633762304
spelling my-uum-etd.52822021-03-18T03:31:20Z Forecasting model for extreme rainfall using artificial neural network 2015 Al-Qurayshi, Yasir Hilal Hadi Ku Mahamud, Ku Ruhana Wan Ishak, Wan Hussain Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Successive days of rainfall are known to cause flood. The forecasting of daily rainfall helps to estimate the occurrences of rainfall and number of wet days, while with a maximum of five consecutive days of rainfall, the magnitude of rainfall within a specified period can predict what may signify rainfall extremes. In this study, data mining and back propagation neural network (BPNN) have been established in developing the extreme rainfall forecasting models. Four forecasting models were developed to forecast the maximum five consecutive days of rainfall amount (PX5D) of the next month. The models only use the extreme rainfall indices outlined by STARDEX as predictors in forecasting. The first developed model uses six extreme rainfall indices in forecasting, the second model uses the values of the PX5D index of a three-month delay, the third model uses the previous six-month PX5D values, while the fourth model was developed to forecast the PX5D using the values of the same index of a twelve-month delay. It was found that when using the six extreme rainfall core indices, the forecasting error was the lowest. A regression model has been developed using the six extreme rainfall indices to compare the performance measurements with the BPNN model that uses the same indices 2015 Thesis https://etd.uum.edu.my/5282/ https://etd.uum.edu.my/5282/1/s815184.pdf text eng public https://etd.uum.edu.my/5282/2/s815184_abstract.pdf text eng public masters masters Universiti Utara Malaysia Abdullah, J. (2013). 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