Artificial Neural Network Flood Prediction for Sungai Isap Residence
A flood is an extremely dangerous disaster that can wipe away an entire city, coastline, and rural area. The flood can cause wide destrotion to property and life that has the supreme corrosive force and can be highly damaging. In order to decrease the damages caused by the flood, an Artificial Neura...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/16371/ http://umpir.ump.edu.my/id/eprint/16371/1/Artificial%20Neural%20Network%20Flood%20Prediction.pdf http://umpir.ump.edu.my/id/eprint/16371/7/fkee-2016-mahfuzah-artificial%20neural1.pdf |
| Summary: | A flood is an extremely dangerous disaster that can wipe away an entire city, coastline, and rural area. The flood can cause wide destrotion to property and life that has the supreme corrosive force and can be highly damaging. In order to decrease the damages caused by the flood, an Artificial Neural Network (ANN) model has been established to predict flood in Sungai Isap, Kuantan, Pahang, Malaysia. This model is able to initiate the same brain thinking process and avoid the influence of the predict judgment. In this paper, presentation and comparison that using Bayesian Regularization (BR) back-propagation, Levenberg-Marquardt (LM) back-propagation and Gradient Descent (GD)back-propagation algorithms will be organized and carry out the result flood prediction. The predicted result of the Bayesian Regularization indicates a satisfactory performance. The conclusions also indicate that Bayesian Regularization is more versatile than Levenberg-Marquart and Gradient Descent with that can be backup or a practical tool for flood prediction. Temperature, precipitation, dew point, humidity, sea level pressure, visibility, wind, and river level data collected from January 2013 until May 2015 in the city of Sungai Isap, Kuantan is used for training, validation, and testing of the network model. The comparison is shown on the basis of mean square error (MSE) and regression (R). The prediction by training function
Bayesian Regularization back-propagation found to be more
suitable to predict flood model.
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