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Water level forecasting using artificial neural network in sungai Pahang, Temerloh

Ainul Afifah, Zakaria (2014) Water level forecasting using artificial neural network in sungai Pahang, Temerloh. Faculty of Civil Engineering and Earth Resources, Universiti Malaysia Pahang.

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Abstract

Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. Current studies have shown that artificial neural networks (ANN) which is a parallel computing model have been successfully applied in water level forecasting studies. (ANN) models require historical data of the subject being study. This data is normally separated into a training dataset and a validation dataset. Several performance measures such as Nash-Sutcliffe efficiency, root mean square error and error distribution are used to evaluate forecasting results. BASIC256 software and Microsoft Excel are other way used to implement to ANN modelling technique. The daily water level data can be taken from the Department of Irrigation and Drainage (DID), Malaysia. Water level forecasting is important for environmental protection and flood control since, when flood events occur, reliable water level forecasts enable the early warning systems to mitigate the flood effects. Importantly, the forecasting model developed based on (ANN) successfully achieves high accuracy forecasting result and satisfactory performance result.

Item Type: Undergraduates Project Papers
Additional Information: Faculty of Civil Engineering and Earth Resources Project paper (Bachelor of Engineering (Civil Engineering)) -- Universiti Malaysia Pahang - 2014
Uncontrolled Keywords: Runoff mathematical models; Rain and rainfall Mathematical models
Subjects: G Geography. Anthropology. Recreation > GB Physical geography
Faculty/Division: Faculty of Civil Engineering & Earth Resources
Depositing User: Mr. Mohd Adzha Mat Sam
Date Deposited: 10 Sep 2015 07:07
Last Modified: 10 Sep 2015 07:07
URI: http://umpir.ump.edu.my/id/eprint/10211
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