Abstract
Abstract 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
Groundwater has been extensively used in many parts of India (Khan et al ; Iqbal et al ) and is considered as the backbone resource for agricultural activities of the country
A correlation analysis was performed to evaluate the capability of the general regression neural network (GRNN) model in predicting groundwater fluctuation
The results show a satisfactory performance of the model in predicting the groundwater fluctuation for all the districts where we have groundwater data, except for the Champawat district
Summary
Groundwater has been extensively used in many parts of India (Khan et al ; Iqbal et al ) and is considered as the backbone resource for agricultural activities of the country. Since the bore well data are not available in other districts of the state, especially in hilly areas, there is a need to have a model for estimating the groundwater fluctuation of those unmonitored areas. The input to the GRNN model is, the GRACE anomaly data, average rainfall, and the average temperature of the area, and the model output is the fluctuation of the groundwater level.
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