Intelligent approach to predict future groundwater level based on artificial neural networks (ANN)
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(2020) 5:51
ORIGINAL PAPER
Intelligent approach to predict future groundwater level based on artificial neural networks (ANN) Malek Derbela1 · Issam Nouiri1 Received: 31 March 2020 / Accepted: 15 July 2020 © Springer Nature Switzerland AG 2020
Abstract To depict hydrogeological variables and understand the physical processes taking place in a complex hydrogeological system, artificial neural networks (ANNs) are widely used as a good alternative approach to tedious numerical models. The aim of this study was to predict the dynamic fluctuations in piezometric levels in Nebhana aquifers (NE Tunisia) using ANNs. A correlation analysis carried out between piezometry, evapotranspiration and rainfall during the period 2000 to 2018 revealed that piezometric levels were influenced by monthly rainfall, evapotranspiration and initial water table level. These informative variables were used as input variables to train the ANN to predict future monthly water table levels for four hydrogeological systems. The minimal and maximal computed relative errors were 0.01 and 19.00%, respectively; root mean square error (RMSE) varied between 0.41 and 2.06; the determination coefficient (R2) ranged between 0.93 and 0.99; and the Nash–Sutcliffe (NASH) efficiency coefficient ranged from 85.32 to 97.82%. To test the generalization capacity of the developed ANN models, we used the ANNs to predict monthly piezometric levels for the period September 2016 to August 2018. The results were satisfactory for all piezometers. Indeed, the minimal and maximal computed RE were − 12.00 and 0.03%, respectively; RMSE was between 0.44 and 1.74; R2 varied between 0.95 and 0.98; the NASH coefficient ranged from 60.00 to 98.99%. These models developed in this study can be adopted for future groundwater level prediction to accurately estimate trends in piezometric levels as well as water pumping costs. Keywords Correlation analysis · Artificial neural network · Groundwater level prediction · Nebhana kairouan/tunisia
Introduction The groundwater reservoir is a complicated system stressed by either natural (climate, hydrology) or entropic (human activities) factors that affect water availability at different time scales. Monitoring groundwater level patterns provides invaluable information for understanding and correcting problems pertaining to withdrawal, assimilation of groundwater behavior and detection of long term trends (Ahmadi and Sedghamiz 2007). Nonetheless, understanding water balances in aquifers constitute is a difficult task for water resource managers, given the non-availability of adequate
Communicated by Mohamed Ksibi, Co-Editor in Chief. * Malek Derbela [email protected] 1
National Agronomic Institute of Tunisia, University of Carthage, Tunis, Tunisia
information and the geographical extent of such natural systems. Calibrated models are needed to simulate and predict future groundwater dynamic fluctuation under different management conditions. Numerical simulation models have been widely used in previous studies (Feng et al. 20
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