Machine Learning and Remote sensing method to Determine the Relationship Between Climate and Groundwater Recharge.

Through machine learning and remote sensing a high end model with finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found out by the application of Random Forest regression followed by the implication of water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is being noted through random forest regression. Thus, the model can be used for various regions of dataset specifically for the area where there is lack of reach for data. It can be successfully used to form a sophisticated end to end ML model.