Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India.

This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the Godavari River basin. The Sacramento model provided initial streamflow estimates for Kanhargaon, Nowrangpur, and Wairagarh. This approach melded traditional hydrological modeling with advanced multi-layer perceptron (MLP) capabilities. When combined with parameters like lagged rainfall, lagged streamflow, potential evapotranspiration (PET), and temperature variations, these initial outputs were further refined using the Sac-MLP model. A comparison with Sacramento revealed the superior performance of the Sac-MLP model. For instance, during training, the Nash Sutcliffe efficiency (NSE) values for the Sac-MLP witnessed an improvement from 0.610 to 0.810 in Kanhargaon, 0.580 to 0.692 in Nowrangpur, and 0.675 to 0.849 in Wairagarh. The results of the testing further corroborated these findings, as evidenced by the increase in the NSE for Kanhargaon from 0.890 to 0.910. Additionally, Nowrangpur and Wairagarh experienced notable improvements, with their NSE values rising from 0.629 to 0.785 and 0.725 to 0.902, respectively. Projections based on EC-Earth3 data across various scenarios highlighted significant shifts in rainfall and temperature patterns, especially in the far future (2071-2100). Regarding the relative change in annual streamflow, Kanhargaon projections under SSP370 and SSP585 for the far future indicate increases of 584.38% and 662.74%. Similarly, Nowrangpur and Wairagarh are projected to see increases of 98.27% and 114.98%, and 81.68% and 108.08%, respectively. This study uses EC-Earth3 estimates to demonstrate the Sac-MLP model's accuracy and importance in climate change water resource planning. The unique method for region-specific hydrological analysis provides vital insights for sustainable water resource management. This research provides a deeper understanding of climate-induced hydrological changes and a robust modeling approach for accurate predictions in changing environmental conditions.

Machine learning approach to evaluate the groundwater quality and human health risk for sustainable drinking and irrigation purposes in South India.

The main purpose of this study was to evaluate the suitability of groundwater for sustainable drinking and irrigation purposes using various indices, such as the nitrate pollution index, agriculture suitability index (ASI), non-carcinogenic human risk assessment (NCHRA), and radial basic function (RBF) model. The novelty of the present study is to develop the ASI model and integrate with RBF model to identify the highly dominating parameter in chemical equilibrium of groundwater. Results showed that >85% of sample locations were suitable for drinking purposes, and the nitrate concentration in groundwater had a negative impact on the overall quality of water. Approximately 12 and 19 sample locations were contaminated owing to the high nitrate concentrations in the study region. The NCHRA study identified that approximately 8.5%, 27.28%, 29.54%, 40.40%, and 28.20% of area was excessively affected during the winter compared to summer season for people 6 to 12 y, 13 to 19 y, 20 to 29 y, 30 to 65 y, and >65 y of age. The RBF model shows that the R2 values for each season were 0.84 and 0.85 during summer and winter, respectively. The north-east and central parts of the study region were found to be more contaminated. The present study identified that, pathway of nitrate contaminant from the agriculture field towards to the sample locations. Overall, parent rock weathering, carbonate ion dissolution, and infiltration of rainwater and leachate from municipal waste dumping yards were the dominant factors influencing the chemical composition of groundwater. The present study achieved the vibrant knowledge about source of contamination, health effect on human body and impact on agriculture uses to develop the cleaner water supply system. The study results will be helpful in enhancing the sustainable action plan for water management in the study area.