A Time-Series Forecasting-Based Prediction Model to Estimate Groundwater Levels in India

India is one of the fast developing countries in the world with a growth rate of 6.4%. Rapid industrialization is the main cause behind such growth. Although industrialization is of utmost importance for growth, sustainability of ecology is also a matter of concern. India has a vast coastline, but the saline water is not suitable for industrialization; so groundwater is the primary source for both industrialization and human consumption. Agriculture plays a major role in India's economy and irrigation is also dependent on groundwater to some extent. Hence the study of groundwater levels is the need of the hour. In this study, time-series techniques like fuzzy time-series analysis and ARIMA are utilized for forecasting monthly groundwater levels. Experiments are performed on the datasets collected from different regions of India. The experimental results demonstrate that fuzzy time series analysis yields more accurate forecast of groundwater levels compared to the ARIMA model. The results of this study can be utilized for planning a suitable policy for groundwater use and its proper regulation to avoid future crisis.