Relating groundwater levels with meteorological parameters using ANN technique

• Published in: Measurement : journal of the International Measurement Confederation Vol. 166; p. 108163
• Main Authors: Iqbal, Mujahid, Ali Naeem, Usman, Ahmad, Afaq, Rehman, Habib-ur, Ghani, Usman, Farid, Tallat
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 15.12.2020; Elsevier Science Ltd
• Subjects: ANN; Artificial intelligence; Artificial neural networks; Back propagation; Back propagation networks; Computer architecture; Data analysis; Groundwater; Groundwater levels; Humidity; Mathematical models; MATLAB; Meteorological factors; Meteorological parameters; Monsoons; Neurons; Rainfall; Relative humidity; Solar radiation; Training; Water table; ANN; Meteorological factors; Groundwater; MATLAB
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2020.108163

•Application of Artificial Neural Network for groundwater levels.•Different types of network architecture of ANN.•Affect of meteorological parameters on groundwater level.•ANN model can successfully predict the groundwater level. In this study, the groundwater occupied within the boundaries of River Ravi and River Sutlej was investigated by using artificial neural networks (ANN) by using the meteorological parameters (data of rainfall (R), maximum temperature (Max.T), minimum temperature (Min.T), solar radiation (S.R.), wind (W), relative humidity (R.H.), the elevation of area (E), polygon area (A) and water table depths (D/W). The best efficient models were studied by using different types of network architecture, such as the number of neurons, hidden layers, and activation function with a different percent of data in training, validation, and testing. The developed Levenberg-Marquardt back-propagation ANN models were compared through statistical performance criteria: Mean Square Error (MSE), Mean Absolute Error (MAE) and Coefficient of determination (R). The results (TT-8-24-1 for pre-monsoon and TT-8-24-1 for post-monsoon) show that ANN model with single hidden layer, 24 neurons, 80% of data for training, 10% for validation, 10% for testing and using tangent sigmoid activation function was found to be optimistic ANN model with MAE, MSE and R values of 0.0338, 0.0023 and 0.97 for pre-monsoon and 0.031, 0.0021 and 0.974 for the case of post-monsoon respectively.