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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2016
|
| Online Access: | http://journalarticle.ukm.my/9565/ http://journalarticle.ukm.my/9565/1/03_Mahmoud_Khaki.pdf |
| _version_ | 1848812131736092672 |
|---|---|
| author | Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin, |
| author_facet | Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin, |
| author_sort | Mahmoud Khaki, |
| building | UKM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T23:57:25Z |
| format | Article |
| id | oai:generic.eprints.org:9565 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T23:57:25Z |
| publishDate | 2016 |
| publisher | Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org: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 |
| spellingShingle | Mahmoud Khaki, Ismail Yusoff, Nur Islami, Nur Hayati Hussin, Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia |
| title | 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_short | 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 |
| url | http://journalarticle.ukm.my/9565/ http://journalarticle.ukm.my/9565/ http://journalarticle.ukm.my/9565/1/03_Mahmoud_Khaki.pdf |