Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

In semi-arid regions, the deterioration in groundwater quality and drop water level upshots importance of resource management for drinking irrigation. Therefore geospatial techniques could be integrated with mathematical models accurate spatiotemporal mapping risk areas at village level. present study, changes level, patterns, future trends were analyzed using eight years (2012–2019) data 171 villages Phagi tehsil, Jaipur district. Kriging interpolation method was used to draw spatial maps pre-monsoon season. These datasets three different time series forecasting (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) Artificial Neural Network prediction parameters. Results reveal that ANN model can describe parameters more accurately than models. The change observed 4.0 m rise 81 during 2012–2013, whereas predicted results 2023–2024 predict no > m. However, based on 2024, will by 6.0 16 Phagi. Assessment index reveals unfit 74% human consumption 2024. This projected micro-level assist decision-makers sustainable management.