Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Shamsuddin, Mohd Khairul Nizar, Mohd Kusin, Faradiella, Sulaiman, Wan Nor Azmin, Ramli, Mohammad Firuz, Tajul Baharuddin, Mohamad Faizal, Adnan, Mohd Shalahuddin
Format: Article
Language:English
Published: EDP Sciences 2017
Subjects:
TD419-428 Water pollution
TK7800-8360 Electronics
Online Access:http://eprints.uthm.edu.my/3409/1/AJ%202017%20%2835%29%20Forecasting%20of%20groundwater%20level.pdf
http://eprints.uthm.edu.my/3409/
https://doi.org/10.1051/matecconf/201710304007
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uthm.eprints.3409
record_format eprints
spelling my.uthm.eprints.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
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic TD419-428 Water pollution
TK7800-8360 Electronics
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
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.
format Article
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
title 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_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_sort forecasting of groundwater level using artificial neural network by incorporating river recharge and river bank infiltration
publisher EDP Sciences
publishDate 2017
url http://eprints.uthm.edu.my/3409/1/AJ%202017%20%2835%29%20Forecasting%20of%20groundwater%20level.pdf
http://eprints.uthm.edu.my/3409/
https://doi.org/10.1051/matecconf/201710304007
_version_ 1738581119803064320
score 13.211869

Similar Items