Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms

Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rain...

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Main Authors:	Mohd Romlay, Muhammad Rabani, Rashid, Muhammad Mahbubur, Toha @ Tohara, Siti Fauziah, Mohd Ibrahim, Azhar
Format:	Article
Language:	English English
Published:	Blue Eyes Intelligence Engineering & Sciences Publication 2019
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/75086/ http://irep.iium.edu.my/75086/ http://irep.iium.edu.my/75086/ http://irep.iium.edu.my/75086/1/IJRTE_2Rainfall-RinoffModelBasedonANNwithLMBRandPSOasLearningAlgorithmsAzhar.pdf http://irep.iium.edu.my/75086/7/75086_Rainfall-Runoff%20Model%20Based%20on%20ANN_SCOPUS.pdf

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spelling	iium-750862019-11-06T13:19:49Z http://irep.iium.edu.my/75086/ Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar T Technology (General) Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfall-runoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficient of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia. Blue Eyes Intelligence Engineering & Sciences Publication 2019-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/75086/1/IJRTE_2Rainfall-RinoffModelBasedonANNwithLMBRandPSOasLearningAlgorithmsAzhar.pdf application/pdf en http://irep.iium.edu.my/75086/7/75086_Rainfall-Runoff%20Model%20Based%20on%20ANN_SCOPUS.pdf Mohd Romlay, Muhammad Rabani and Rashid, Muhammad Mahbubur and Toha @ Tohara, Siti Fauziah and Mohd Ibrahim, Azhar (2019) Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms. International Journal of Recent Technology and Engineering (IJRTE), 8 (3). pp. 971-979. ISSN 2277-3878 https://www.ijrte.org/wp-content/uploads/papers/v8i3/C4115098319.pdf 10.35940/ijrte.C4115.098319
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms
description	Rainfall-runoff model requires comprehensive computation as its relation is a complex natural phenomenon. Various inter-related processes are involved with factors such as rainfall intensity, geomorphology, climatic and landscape are all affecting runoff response. In general there is no single rainfall-runoff model that can cater to all flood prediction system with varying topological area. Hence, there is a vital need to have custom-tailored prediction model with specific range of data, type of perimeter and antecedent hour of prediction to meet the necessity of the locality. In an attempt to model a reliable rainfall-runoff system for a flood-prone area in Malaysia, 3 different approach of Artificial Neural Networks (ANN) are modelled based on the data acquired from Sungai Pahang, Pekan. In this paper, the ANN rainfall-runoff models are trained by the Levenberg Marquardt (LM), Bayesian Regularization (BR) and Particle Swarm Optimization (PSO). The performances of the learning algorithms are compared and evaluated based on a 12-hour prediction model. The results demonstrate that LM produces the best model. It outperforms BR and PSO in terms of convergence rate, lowest mean square error (MSE) and optimum coefficient of correlation. Furthermore, the LM approach are free from overfitting, which is a crucial concern in conventional ANN learning algorithm. Our case study takes the data of rainfall and runoff from the year 2012 to 2014. This is a case study in Pahang river basin, Pekan, Malaysia.
format	Article
author	Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar
author_facet	Mohd Romlay, Muhammad Rabani Rashid, Muhammad Mahbubur Toha @ Tohara, Siti Fauziah Mohd Ibrahim, Azhar
author_sort	Mohd Romlay, Muhammad Rabani
title	Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms
title_short	Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms
title_full	Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms
title_fullStr	Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms
title_full_unstemmed	Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms
title_sort	rainfall-runoff model based on ann with lm, br and pso as learning algorithms
publisher	Blue Eyes Intelligence Engineering & Sciences Publication
publishDate	2019
url	http://irep.iium.edu.my/75086/ http://irep.iium.edu.my/75086/ http://irep.iium.edu.my/75086/ http://irep.iium.edu.my/75086/1/IJRTE_2Rainfall-RinoffModelBasedonANNwithLMBRandPSOasLearningAlgorithmsAzhar.pdf http://irep.iium.edu.my/75086/7/75086_Rainfall-Runoff%20Model%20Based%20on%20ANN_SCOPUS.pdf
first_indexed	2023-09-18T21:46:15Z
last_indexed	2023-09-18T21:46:15Z
_version_	1777413469781360640

Rainfall-runoff model based on ANN with LM, BR and PSO as learning algorithms

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