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Modeling hydrogeologic processes facilitates in accurate prediction/forecasting of groundwater level variations. Still, the uncertainty in model prediction is a major concern that requires detailed investigation. There could be several factors which introduce uncertainty such as inherent assumption, various levels of model complexity and simplicity. In general, model inputs, parameters and structure are the major sources of uncertainty while quantifying model prediction uncertainty. In this study, a genetic programming (GP) based models have been employed for forecasting groundwater level variation along with prediction uncertainty quantification. Though various sources induce uncertainty in the model prediction, the input uncertainty quantification has received little attention. Hence, the input uncertainty has been considered for the analysis in this study. The proposed method is demonstrated using measured monthly values of rainfall and corresponding groundwater level data of Amarawathi basin, India. It is observed that the prediction along with uncertainty quantification improves the confidence level of models while making decisions, in particular for effective planning and management of groundwater resources.

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Land use and land cover (LULC) change analysis and forecasting aids the upcoming generation in research and evaluate the global climate change for managing and controlling environmental sustainability. This research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classi ed in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. The CA-Markov's Land change modeler (LCM) was set up to create future LULC. It was carried out in four steps: (1) Change analysis, (2) Transition potential, (3) Change prediction, and (4) Model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are under stress and developing into built-up. The overall accuracy was above 89%, and the kappa coe cient was above 87% for all three years. This region is highly susceptible to inland oods, coastal oods, and other natural disasters; thus, this study's results support future development plans and decision-making.