Abstract
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.
Highlights
River Bank Infiltration (RBI) is a natural filter process to improve the drinking water quality obtained from surface water flow through aquifer and mixture with groundwater
The artificial neural networks (ANNs) model was designed to predict groundwater levels in two test wells with 1 day a-head time using a set of suitable input parameters
The input parameters for the ANN model were decided by considering the parameters potentially to affecting the groundwater level
Summary
River Bank Infiltration (RBI) is a natural filter process to improve the drinking water quality obtained from surface water flow through aquifer and mixture with groundwater. Surface waters are infiltrated through the aquifer media in the pumping wells during pumping activities and subsequently influence river and groundwater level [1,2,3,4,5,6,7] stated that river and aquifer interactions are exist when waters from vertical and horizontal wells that are located in alluvial adjacent to rivers/lakes are pumped. It must be managed in an integrated way to provide efficient water supplies and concerns over the conservation of the natural environment. The other factors such as precipitation and stream flow can influence quickly the river water depth
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