Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality pattern. Alternatively, deseasonalized autoregressive and moving average models (Ds-ARMA) can be modeled with deseasonalized groundwater level signals in which the seasonal component is estimated and removed from the raw groundwater level signals. The seasonal component is traditionally estimated by calculating long-term averaging values of the corresponding months in the year. This traditional way of estimating seasonal component may not be appropriate for non-stationary groundwater level signals. Thus, in this study, an improved way of estimating the seasonal component by adopting a 13-month moving average trend and corresponding confidence interval approach has been attempted. To test the proposed approach, two representative observation wells from Adyar basin, India were modeled by both traditional and proposed methods. It was observed from this study that the proposed model prediction performance was better than the traditional model’s performance with R2 values of 0.82 and 0.93 for the corresponding wells’ groundwater level data.

Discrete groundwater level datasets are interpolated often using kriging group of models to produce a spatially continuous groundwater level map. There is always some level of uncertainty associated with different interpolation methods. Therefore, we developed a new trend function with the mean groundwater level as a drift variable in the regression kriging approach to predict the groundwater levels at the unvisited locations. Groundwater level data for 29 observation wells in Adyar River Basin were used to assess the performance of the developed regression kriging models. The cross-validation results show that the proposed regression kriging method in the spatial domain outperforms other physical and kriging-based methods with R2 values of 0.96 and 0.98 during pre-monsoon and post-monsoon seasons, respectively.

The objective of this research is to develop a fuzzy-based groundwater sustainability index (FGSI) model to evaluate the sustainability of groundwater system at selected cities in Asian.   The new Mamdani type fuzzy-based inference system known as FGSI was developed. It contains five components and twenty-four indicators, which covers five dimensions of sustainability, namely, environmental, social, economic, mutual trust, and institutional. The FGSI model offers a novel combination of indicators, which covers aspects of groundwater quality, quantity, and management. An attempt was made to develop a robust index for estimating the groundwater sustainability. The model was evaluated for selected cities in Asian with different defuzzification methods and compared with the conventional method. The centroid defuzzification method produced well diversified results compared with other methods, including conventional method. The overall groundwater sustainability of Hyderabad of India was estimated as highly sustainable and, Lahore of Pakistan, Bangkok of Thailand, Ho Chi Minh City of Vietnam, and Yangon City of Myanmar were estimated as moderately sustainable. The FGSI model may help to policy and decision makers to provide a reliable and resilient sustainable management system in the cities by identifying the indicators for the improvement.