Investigating Spatio-temporal behaviour for Groundwater in North-West India: A Deep Learning Approach

Abstract

Abstract Sustainability and conservation of natural resources require amalgamation of the novel approaches with the current usages and weather conditions. Groundwater resources is one of the natural resources which is varying annually and requires regular attention for the prediction. Our proposed deep learning (DL) approach, namely Convolution Neural Network-Long short term memory (ConvLSTM) has been implemented for the groundwater level (GWL) prediction model. The model is designed based on U-Net framework with up-sampling and down-sampling modules and also induces non-linearity using the ReLU activation function. Each module in the LSTM unit is responsible for pattern recognition based on the temporal information of GWL. The assessment of the groundwater in North-West India (NWI) has been carried out using several fundamental factors such as precipitation, soil moisture, evapotranspiration and satellite-based groundwater storage. In addition, in-situ groundwater has been used to get groundwater fluctuation scenarios (i.e., categorised into four cycles PrePre, PrePost, PostPre, and PostPost) w.r.t monsoon season to understand the difference (Δh) in GWL. The proposed model has been tested with other DL frameworks such as; Artificial neural network (ANN) and Convolution neural network (CNN). The model has been trained using the stochastic gradient method to optimise the internal parameter and validated using several geo-locations information of NWI, where ConvLSTM outperformed compared to the benchmark method. The proposed model has shown consistent least error in terms of root mean square root (RMSE) and mean square error (MAE) for the year 2014-17 with an overall score of 0.0957 and 0.0520, respectively.