Rainfall runoff model using probability distributed model (PDM)

Probability Distributed Model (PDM) is widely used in analyzing the hydrological behavior. One of the applications is involved in rainfall runoff modelling to forecast the occuring of flood in Johor Bahru, Malaysia. In this study, Pareto distributed is used to represent the storage capacity of PDM....

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主要作者: Yusof, Yazre
格式: Thesis
出版: 2014
主題:
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author Yusof, Yazre
author_facet Yusof, Yazre
author_sort Yusof, Yazre
description Probability Distributed Model (PDM) is widely used in analyzing the hydrological behavior. One of the applications is involved in rainfall runoff modelling to forecast the occuring of flood in Johor Bahru, Malaysia. In this study, Pareto distributed is used to represent the storage capacity of PDM. By providing the best fit between observed and simulated discharges, a Genetic Algorithm (GA) method has been applied to optimize the optimal parameter of PDM. The performance of PDM is accessed through the calibration and validation of different data with th same parameter. The model was applied to Sungai Pengeli (Station B) and the performance was assessed using the values R-squared value. A strong relationship between observed and calculated discharge was detected. in order to forecast water level days ahead, the error prediction employs the Autoregressive Moving Average (ARMA) model which is one of the model of time series. From the analysis, the trend of change in water level of station Sungai Pengeli (Station B) is quite accurately captured by using PDM with to small differences of the water level between actual and forecast values.
format Thesis
id uthm-41712
institution Universiti Teknologi Malaysia
publishDate 2014
record_format eprints
spelling uthm-417122017-08-22T04:37:20Z http://eprints.utm.my/41712/ Rainfall runoff model using probability distributed model (PDM) Yusof, Yazre QA Mathematics Probability Distributed Model (PDM) is widely used in analyzing the hydrological behavior. One of the applications is involved in rainfall runoff modelling to forecast the occuring of flood in Johor Bahru, Malaysia. In this study, Pareto distributed is used to represent the storage capacity of PDM. By providing the best fit between observed and simulated discharges, a Genetic Algorithm (GA) method has been applied to optimize the optimal parameter of PDM. The performance of PDM is accessed through the calibration and validation of different data with th same parameter. The model was applied to Sungai Pengeli (Station B) and the performance was assessed using the values R-squared value. A strong relationship between observed and calculated discharge was detected. in order to forecast water level days ahead, the error prediction employs the Autoregressive Moving Average (ARMA) model which is one of the model of time series. From the analysis, the trend of change in water level of station Sungai Pengeli (Station B) is quite accurately captured by using PDM with to small differences of the water level between actual and forecast values. 2014 Thesis NonPeerReviewed Yusof, Yazre (2014) Rainfall runoff model using probability distributed model (PDM). Masters thesis, Universiti Teknologi Malaysia, Faculty of Science.
spellingShingle QA Mathematics
Yusof, Yazre
Rainfall runoff model using probability distributed model (PDM)
title Rainfall runoff model using probability distributed model (PDM)
title_full Rainfall runoff model using probability distributed model (PDM)
title_fullStr Rainfall runoff model using probability distributed model (PDM)
title_full_unstemmed Rainfall runoff model using probability distributed model (PDM)
title_short Rainfall runoff model using probability distributed model (PDM)
title_sort rainfall runoff model using probability distributed model pdm
topic QA Mathematics
url-record http://eprints.utm.my/41712/
work_keys_str_mv AT yusofyazre rainfallrunoffmodelusingprobabilitydistributedmodelpdm