Assessment of groundwater level using satellite-based hydrological parameters in North-West India: A deep learning approach

Groundwater (GW) has been prominent source of freshwater for sustainable growth of agriculture, water management and urban/industrial purposes. The overexploitation of the water source leads to large variation of groundwater level (GWL) in the Indian sub-continent. The GWL mostly fluctuates via several factors like; groundwater extraction, precipitation, soil moisture, evaporation, etc., and prediction of GWL by collecting these factor for large geographical region is a challenging task. In the study, deep learning (DL) approach, namely Convolution Neural Network-Long short term memory (ConvLSTM) model has been implemented for prediction of the GWL. The model is designed based on the U-Net framework with the integration LSTM unit, to process the spatiotemporal information induced by the GWL factors between the years 2005–2017. The assessment of the GW in North-West India (NWI) has been carried out using several aforementioned hydrological parameters, selected based on correlation. In addition, in-situ groundwater has been used to get GW fluctuation scenarios (i.e., categorised into four cycles PrePre, PrePost, PostPre, and PostPost) w.r.t monsoon season to predict the difference (Δh) in GWL. The proposed model has been tested w.r.t Artificial neural network (ANN) and Convolution neural network (CNN) and cross-validate using several geo-locations information of NWI. The ConvLSTM has outperformed based on overall root means square (RMSE) error of 0.1099, 0.1082, 0.1005 and 0.0957 for each cycle i.e. PrePre, PrePost, PostPre, and PostPost, respectively, compared to ANN and CNN.