MATEC Web Conf.
Volume 103, 2017International Symposium on Civil and Environmental Engineering 2016 (ISCEE 2016)
|Number of page(s)||11|
|Section||Urban Hydrology and Hydraulic Research|
|Published online||05 April 2017|
Forecasting of Groundwater Level using Artificial Neural Network by incorporating river recharge and river bank infiltration
1 Hydrogeology Research Centre, National Hydraulic Research Institute of Malaysia, Lot 5377, Jalan Putra Permai, 43300 Seri Kembangan, Selangor, Malaysia
2 Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
3 Department of Environmental and Water Resources, Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia, Johor
* Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences, 2017
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