Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO₃⁻), Magnesium (Mg²⁺), Sulphate (SO₄²⁻), Calcium (Ca²⁺), and Potassium (K⁺). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R² (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

This research proposes an integrated methodology for incorporating RS, GIS and AHP techniques for the assessment of agricultural land suitability. In Kadapa district, Andhra Pradesh, India, study is being done on how to best promote agriculture as a source of income to boost the economy of the region. A combined RS, GIS and AHP techniques has been utilized that incorporates organizing AHP hierarchy, criteria specification, pairwise comparison, and criterion map preparation. Land suitability comparison showed that an area of 4.42 km2 (2.62%) is appropriate for irrigation, while an area of 54.39 km2 (32.33%) is appropriate moderately suitable for rainfed agriculture and 95.76 km2 (56.93%) is marginally suitable for agricultural productions. About 13.64 km2 (8.11%) land is currently not suitable for agricultural production. Additionally, the analysis clearly shows the necessity of a decrease in irrigated agricultural land and an increase in dry farm agricultural land. This application of RS, GIS and AHP based agricultural land suitability analysis is helpful in referring agricultural activities to the areas with good physical and environmental conditions, allowing maximum agricultural efficiency in the countryside, increasing non-agricultural uses in areas with low efficiency, and avoiding the construction and environmental pressures on suitable farmland.