GROUNDWATER FORECASTING ANALYSIS USING MACHINE LEARNING

Abstract

The system that is proposed is used to analyse groundwater level data in one or more states. This system involves analysing the data for more than 100 observation wells in each of these states and to develop seasonal models to represent the groundwater behaviour. Three different type of models to implement are periodic, polynomial and rainfall models. While periodic and polynomial models capture trends on water levels in observation wells, the rainfall model explores the correlation between the rainfall levels and water levels. The periodic and polynomial models are developed only using the groundwater level data of observation wells while the rainfall model also uses the rainfall data.