Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan

Flooding is a natural disaster which has been occurring annually throughout the whole world. The disaster, such as other natural catastrophe could only be mitigated rather than it being completely solved. Runoff prediction proved to be very vital in pre-flooding management system. In recent years, A...

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Main Authors:	M. Romlay, M. Rabani, Rashid, Muhammad Mahbubur, Toha @ Tohara, Siti Fauziah
Format:	Conference or Workshop Item
Language:	English English
Published:	The Institute of Electrical and Electronics Engineers, Inc. 2016
Subjects:	TD194 Environmental effects of industries and plants
Online Access:	http://irep.iium.edu.my/58177/ http://irep.iium.edu.my/58177/ http://irep.iium.edu.my/58177/1/58177_Development%20of%20Particle%20Swarm.pdf http://irep.iium.edu.my/58177/2/58177_Development%20of%20Particle%20Swarm_SCOPUS.pdf

id	iium-58177
recordtype	eprints
spelling	iium-581772017-09-27T08:58:23Z http://irep.iium.edu.my/58177/ Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan M. Romlay, M. Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah TD194 Environmental effects of industries and plants Flooding is a natural disaster which has been occurring annually throughout the whole world. The disaster, such as other natural catastrophe could only be mitigated rather than it being completely solved. Runoff prediction proved to be very vital in pre-flooding management system. In recent years, Artificial Neural Network has been applied in various prediction models of hydrological system. It is proposed to model the rainfall-runoff system of Pahang River in Pekan. Mean rainfall data of 5 hydrological stations are used as the input and water level data as the output. The Artificial Neural Networks are trained with Particle Swarm Optimization. The performances of Artificial Neural Networks were measured with Ackley cost function value. Neural network configuration of 450 number of maximum iteration, 6 number of particles and 1.9 and 2.0 values of Particle Swarm Optimization parameter constant for global best (c1) and Particle Swarm Optimization constant for personal best (c2) respectively shows the highest global best function value. The neural network configuration of 300 number of maximum iteration, 3 numbers of particles and 2.2 value of (c1) and (c2) produces lowest global best function value. The output shows Artificial Neural Network trained by Particle Swarm Optimization can successfully model rainfall-runoff. © 2016 IEEE. The Institute of Electrical and Electronics Engineers, Inc. 2016 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/58177/1/58177_Development%20of%20Particle%20Swarm.pdf application/pdf en http://irep.iium.edu.my/58177/2/58177_Development%20of%20Particle%20Swarm_SCOPUS.pdf M. Romlay, M. Rabani and Rashid, Muhammad Mahbubur and Toha @ Tohara, Siti Fauziah (2016) Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan. In: 6th International Conference on Computer and Communication Engineering (ICCCE 2016), 25th-27th July 2016, Kuala Lumpur. http://ieeexplore.ieee.org/document/7808330/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TD194 Environmental effects of industries and plants
spellingShingle	TD194 Environmental effects of industries and plants M. Romlay, M. Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan
description	Flooding is a natural disaster which has been occurring annually throughout the whole world. The disaster, such as other natural catastrophe could only be mitigated rather than it being completely solved. Runoff prediction proved to be very vital in pre-flooding management system. In recent years, Artificial Neural Network has been applied in various prediction models of hydrological system. It is proposed to model the rainfall-runoff system of Pahang River in Pekan. Mean rainfall data of 5 hydrological stations are used as the input and water level data as the output. The Artificial Neural Networks are trained with Particle Swarm Optimization. The performances of Artificial Neural Networks were measured with Ackley cost function value. Neural network configuration of 450 number of maximum iteration, 6 number of particles and 1.9 and 2.0 values of Particle Swarm Optimization parameter constant for global best (c1) and Particle Swarm Optimization constant for personal best (c2) respectively shows the highest global best function value. The neural network configuration of 300 number of maximum iteration, 3 numbers of particles and 2.2 value of (c1) and (c2) produces lowest global best function value. The output shows Artificial Neural Network trained by Particle Swarm Optimization can successfully model rainfall-runoff. © 2016 IEEE.
format	Conference or Workshop Item
author	M. Romlay, M. Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah
author_facet	M. Romlay, M. Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah
author_sort	M. Romlay, M. Rabani
title	Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan
title_short	Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan
title_full	Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan
title_fullStr	Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan
title_full_unstemmed	Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan
title_sort	development of particle swarm optimization based rainfall-runoff prediction model for pahang river, pekan
publisher	The Institute of Electrical and Electronics Engineers, Inc.
publishDate	2016
url	http://irep.iium.edu.my/58177/ http://irep.iium.edu.my/58177/ http://irep.iium.edu.my/58177/1/58177_Development%20of%20Particle%20Swarm.pdf http://irep.iium.edu.my/58177/2/58177_Development%20of%20Particle%20Swarm_SCOPUS.pdf
first_indexed	2023-09-18T21:22:15Z
last_indexed	2023-09-18T21:22:15Z
_version_	1777411959265689600

Development of particle swarm optimization based rainfall-runoff prediction model for Pahang River, Pekan

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