Khoo, Chun Keong and Mahfuzah, Mustafa and Ahmad Johari, Mohamad and M. H., Sulaiman and Nor Rul Hasma, Abdullah (2016) Artificial Neural Network Flood Prediction for Sungai Isap Residence. In: 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS2016). , 22 October 2016 , Shah Alam, Malaysia. pp. 1-6.. (Unpublished)
![[img]](http://umpir.ump.edu.my/style/images/fileicons/application_pdf.png) |
PDF
Artificial Neural Network Flood Prediction.pdf Restricted to Registered users only Download (549kB) | Request a copy |
|
|
PDF
fkee-2016-mahfuzah-artificial neural1.pdf Download (75kB) | Preview |
Abstract
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.
| Item Type: | Conference or Workshop Item (Speech) |
|---|---|
| Uncontrolled Keywords: | extremely dangerous disaster, flood, highly damaging, supreme corrosive force, Artificial Neural Network |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 14 Apr 2017 02:40 |
| Last Modified: | 11 Apr 2018 01:19 |
| URI: | http://umpir.ump.edu.my/id/eprint/16371 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |