GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data

Springs, the primary source of water in the Indian state of Uttarakhand, are disappearing day by day. A report published by United Nations Development Program in 2015 indicates that due to deforestation, and forest fire, the groundwater of the state has been reduced by 50% between 2007 and 2010. As such, for taking proper adaptation policies for the state, it is necessary to monitor the state's groundwater fluctuation. Unfortunately, the bore well data are very limited. Thus, we are proposing two general regression neural network (GRNN)-based models for fast estimation of groundwater fluctuation. The first model evaluates and predicts the groundwater fluctuation in the five known bore well data districts of the state, and the second model, which is based on the first model along with a correlation matrix, predicts the groundwater fluctuation in the districts where no bore well data are available. The assessment of the results shows that the proposed GRNN-based model is capable of estimating the groundwater fluctuation both in the areas where bore well data are available and the areas where bore well data are not available. The study shows that there is a sharp decline in the groundwater level in the hilly districts of the state. HIGHLIGHTS A methodology has been proposed to obtain the regional groundwater fluctuation using GRACE data.; The model is also capable of deriving the groundwater fluctuation for the areas where bore well data is not available.; The model uses GRACE data along with rainfall and temperature for the prediction of groundwater fluctuation.