Forecasting land-use and land-cover change for groundwater sustainability in the Muvattupuzha basin using CA-Markov...

Rapid urbanization and land use and land cover (LULC) change have affected groundwater dynamics and its quality in many river basins. The present study uses an integrated framework combining multi-temporal Landsat imagery, geospatial analysis, multivariate statistics, and Machine Learning (ML) approaches to understand LULC changes and groundwater dynamics and its quality degradation.  The supervised classification was used in the present study, which shows that built-up land increased significantly from 12.3% (329.13 km2) in 2003 to 44.4% (1,187.11 km2) in 2023, mainly due to the conversion of agricultural and forested land. Furthermore, future LULC dynamics by the CA-Markov model indicate continuous landscape transformation, with net conversions into built-up and forested areas during the periods 2023–2033 and 2033–2043, respectively, while there is a decline in water bodies and agricultural land use, and their rates of change stabilize over the periods approaching 2043–2050. Multivariate statistical analyses, such as correlation analysis, Principal Component Analysis (PCA), and Cluster Analysis, identify both geogenic processes and human activities as dominant determinants of groundwater hydrochemistry. To investigate the relationships between physicochemical parameters and nitrate variability, 3 ML models were employed: Random Forest (RF), Support Vector Regression (SVR), and XGBoost. Model interpretation using SHapley Additive exPlanations (SHAP) showed that Mg2+, Ca2+, and alkalinity are the significant factors influencing nitrate distribution, reflecting buffering reactions and redox-controlled processes. An integrated framework combining LULC, hydrogeochemical, and ML techniques provides a strong foundation for assessing groundwater. It offers insights into sustainable land-use planning and groundwater management in rapidly urbanizing tropical basins.