Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration
Groundwater tables forecasting during implemented river bank infiltration (RBI) method is important to identify adequate storage of groundwater aquifer for water supply purposes. This study illustrates the development and application of artificial neural networks (ANNs) to predict groundwater ta...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2017
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3409/ http://eprints.uthm.edu.my/3409/1/AJ%202017%20%2835%29%20Forecasting%20of%20groundwater%20level.pdf |
| _version_ | 1848888011245223936 |
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| author | Shamsuddin, Mohd Khairul Nizar Mohd Kusin, Faradiella Sulaiman, Wan Nor Azmin Ramli, Mohammad Firuz Tajul Baharuddin, Mohamad Faizal Adnan, Mohd Shalahuddin |
| author_facet | Shamsuddin, Mohd Khairul Nizar Mohd Kusin, Faradiella Sulaiman, Wan Nor Azmin Ramli, Mohammad Firuz Tajul Baharuddin, Mohamad Faizal Adnan, Mohd Shalahuddin |
| author_sort | Shamsuddin, Mohd Khairul Nizar |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Groundwater tables forecasting during implemented river bank
infiltration (RBI) method is important to identify adequate storage of
groundwater aquifer for water supply purposes. This study illustrates the
development and application of artificial neural networks (ANNs) to
predict groundwater tables in two vertical wells located in confined aquifer
adjacent to the Langat River. ANN model was used in this study is based
on the long period forecasting of daily groundwater tables. ANN models
were carried out to predict groundwater tables for 1 day ahead at two
different geological materials. The input to the ANN models consider of daily rainfall, river stage, water level, stream flow rate, temperature and groundwater level. Two different type of ANNs structure were used to predict the fluctuation of groundwater tables and compared the best forecasting values. The performance of different models structure of the ANN is used to identify the fluctuation of the groundwater table and provide acceptable predictions. Dynamics prediction and time series of the system can be implemented in two possible ways of modelling. The coefficient correlation (R), Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient determination (R2) were chosen as the selection criteria of the best model. The statistical values for DW1 are 0.8649, 0.0356, 0.01, and 0.748 respectively. While for DW2 the statistical values are 0.7392, 0.0781, 0.0139, and 0.546 respectively. Based on these results, it clearly shows that accurate predictions can be achieved with time series 1-day ahead of forecasting groundwater table and the interaction between river and aquifer can be examine. The findings of the study can be used to assist policy marker to manage groundwater resources by using RBI method. |
| first_indexed | 2025-11-15T20:03:30Z |
| format | Article |
| id | uthm-3409 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:03:30Z |
| publishDate | 2017 |
| publisher | EDP Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-34092021-11-17T03:50:35Z http://eprints.uthm.edu.my/3409/ Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration Shamsuddin, Mohd Khairul Nizar Mohd Kusin, Faradiella Sulaiman, Wan Nor Azmin Ramli, Mohammad Firuz Tajul Baharuddin, Mohamad Faizal Adnan, Mohd Shalahuddin TD419-428 Water pollution TK7800-8360 Electronics Groundwater tables forecasting during implemented river bank infiltration (RBI) method is important to identify adequate storage of groundwater aquifer for water supply purposes. This study illustrates the development and application of artificial neural networks (ANNs) to predict groundwater tables in two vertical wells located in confined aquifer adjacent to the Langat River. ANN model was used in this study is based on the long period forecasting of daily groundwater tables. ANN models were carried out to predict groundwater tables for 1 day ahead at two different geological materials. The input to the ANN models consider of daily rainfall, river stage, water level, stream flow rate, temperature and groundwater level. Two different type of ANNs structure were used to predict the fluctuation of groundwater tables and compared the best forecasting values. The performance of different models structure of the ANN is used to identify the fluctuation of the groundwater table and provide acceptable predictions. Dynamics prediction and time series of the system can be implemented in two possible ways of modelling. The coefficient correlation (R), Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient determination (R2) were chosen as the selection criteria of the best model. The statistical values for DW1 are 0.8649, 0.0356, 0.01, and 0.748 respectively. While for DW2 the statistical values are 0.7392, 0.0781, 0.0139, and 0.546 respectively. Based on these results, it clearly shows that accurate predictions can be achieved with time series 1-day ahead of forecasting groundwater table and the interaction between river and aquifer can be examine. The findings of the study can be used to assist policy marker to manage groundwater resources by using RBI method. EDP Sciences 2017 Article PeerReviewed text en http://eprints.uthm.edu.my/3409/1/AJ%202017%20%2835%29%20Forecasting%20of%20groundwater%20level.pdf Shamsuddin, Mohd Khairul Nizar and Mohd Kusin, Faradiella and Sulaiman, Wan Nor Azmin and Ramli, Mohammad Firuz and Tajul Baharuddin, Mohamad Faizal and Adnan, Mohd Shalahuddin (2017) Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration. MATEC Web Conference, 103. pp. 1-11. ISSN 2261-236X https://doi.org/10.1051/matecconf/201710304007 |
| spellingShingle | TD419-428 Water pollution TK7800-8360 Electronics Shamsuddin, Mohd Khairul Nizar Mohd Kusin, Faradiella Sulaiman, Wan Nor Azmin Ramli, Mohammad Firuz Tajul Baharuddin, Mohamad Faizal Adnan, Mohd Shalahuddin Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration |
| title | Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration |
| title_full | Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration |
| title_fullStr | Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration |
| title_full_unstemmed | Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration |
| title_short | Forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration |
| title_sort | forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration |
| topic | TD419-428 Water pollution TK7800-8360 Electronics |
| url | http://eprints.uthm.edu.my/3409/ http://eprints.uthm.edu.my/3409/ http://eprints.uthm.edu.my/3409/1/AJ%202017%20%2835%29%20Forecasting%20of%20groundwater%20level.pdf |