Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia

Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (ANN) was to investigate the feasibility of fee...

Full description

Bibliographic Details
Main Authors:	Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin
Format:	Article
Language:	English
Published:	Universiti Kebangsaan Malaysia 2016
Online Access:	http://journalarticle.ukm.my/9565/ http://journalarticle.ukm.my/9565/ http://journalarticle.ukm.my/9565/1/03_Mahmoud_Khaki.pdf

id	ukm-9565
recordtype	eprints
spelling	ukm-95652016-12-14T06:50:19Z http://journalarticle.ukm.my/9565/ Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin, Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (ANN) was to investigate the feasibility of feed-forward, Elman and Cascade forward neural networks with different algorithms to estimate groundwater levels in the Langat Basin from 2007 to 2013. In order to examine the accuracy of monthly water level forecasts, effectiveness of the steepness coefficient in the sigmoid function of a developed ANN model was evaluated in this research. The performance of the models was evaluated using the mean squared error (MSE) and the correlation coefficient (R). The results indicated that the ANN technique was well suited for forecasting groundwater levels. All models developed had shown acceptable results. Based on the observation, the feed-forward neural network model optimized with the Levenberg-Marquardt algorithms showed the most beneficial results with the minimum MSE value of (0.048) and maximum R value of (0.839), obtained for simulation of groundwater levels. The present research conclusively showed the capability of ANNs to provide excellent estimation accuracy and valuable sensitivity analyses. Universiti Kebangsaan Malaysia 2016-01 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/9565/1/03_Mahmoud_Khaki.pdf Mahmoud Khaki, and Ismail Yusoff, and Nur Islami, and Nur Hayati Hussin, (2016) Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia. Sains Malaysiana, 45 (1). pp. 19-28. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid45bil1_2016/KandunganJilid45Bil1_2016.htm
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (ANN) was to investigate the feasibility of feed-forward, Elman and Cascade forward neural networks with different algorithms to estimate groundwater levels in the Langat Basin from 2007 to 2013. In order to examine the accuracy of monthly water level forecasts, effectiveness of the steepness coefficient in the sigmoid function of a developed ANN model was evaluated in this research. The performance of the models was evaluated using the mean squared error (MSE) and the correlation coefficient (R). The results indicated that the ANN technique was well suited for forecasting groundwater levels. All models developed had shown acceptable results. Based on the observation, the feed-forward neural network model optimized with the Levenberg-Marquardt algorithms showed the most beneficial results with the minimum MSE value of (0.048) and maximum R value of (0.839), obtained for simulation of groundwater levels. The present research conclusively showed the capability of ANNs to provide excellent estimation accuracy and valuable sensitivity analyses.
format	Article
author	Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin,
spellingShingle	Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin, Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
author_facet	Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin,
author_sort	Mahmoud Khaki,
title	Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
title_short	Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
title_full	Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
title_fullStr	Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
title_full_unstemmed	Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia
title_sort	artificial neural network technique for modeling of groundwater level in langat basin, malaysia
publisher	Universiti Kebangsaan Malaysia
publishDate	2016
url	http://journalarticle.ukm.my/9565/ http://journalarticle.ukm.my/9565/ http://journalarticle.ukm.my/9565/1/03_Mahmoud_Khaki.pdf
first_indexed	2023-09-18T19:55:15Z
last_indexed	2023-09-18T19:55:15Z
_version_	1777406485892956160

Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia

Similar Items