Predicting future groundwater recharge scenario in the Punjab region of India using machine learning techniques

Over last few decades the Punjab region of India has been one of the country's leading contributors to agricultural products. The agricultural farms in the region are supplied with water from a well-established canal system and groundwater reserve in the state. The share of irrigated area in the region fed by canals and groundwater wells are 28 and 72%, respectively. The over and unscientific usage of groundwater over the years has resulted in groundwater depletion at an alarming rate. To help policymakers address the situation and develop effective plans, forecasting groundwater recharge for the future is utmost essential. The recharge process primarily governs the growth or depletion of groundwater reserve. Groundwater recharge is one of the most difficult phenomena to be quantified as it cannot be measured directly and is influenced by several processes varying spatially and temporally. Extensive research work for quantifying the groundwater recharge have been performed in the past. These investigations introduced a number of methodologies, including chemical tracers and physical procedures. These methods, however, being experimental in nature, involve significant time and investment. The use of machine learning algorithms to predict the recharge is promoted as a solution to these problems. These algorithms have proven to be efficient enough to deduce the recharge with very high accuracy. Through a variety of models, ranging from the most basic to one of the more intricate, we have attempted to forecast the recharge scenario in the Punjab region, India. Four machine learning algorithms, namely the Multi-linear regression model, Non-linear regression model (Random Forest), Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) have been employed in this study. The aim was to comprehend the dependence of groundwater recharge on the factors of temperature, precipitation, soil type, LULC, and ground slope. The observed recharge for every subsequent month in a 30-year period is calculated using the observed monthly groundwater level data from observation wells located throughout Punjab. The monthly temperature and precipitation data are used for the study while soil type and ground slope for the location of the observation stations are extracted from digital elevation models (DEMs). At intervals of three years, the LULC maps are created. The models are then used to forecast and compare with the available observation data after the entire data set was split into a training and testing set using the 80/20 method. The models were then assessed for their ability to predict observational data using the Root Mean Squared Error (RMSE) and Coefficient of Determination (R²) in each case. The groundwater recharge prediction is then performed using the model with the highest accuracy.