Y V Sai Bhageerath

Accurate surface water monitoring across India’s diverse landscapes is often hindered by the limitations of pixel-based classification and static indexing. This study introduces an object-based framework integrating DBSCAN clustering with supervised learning classification for automated water extraction using Sentinel-2 imagery. To ensure regional generalizability, the model was trained on a multi-site dataset spanning 20 distinct geographical locations across India. Out of 5 machine learning (ML)models, XGBoost outperformed other ML architectures, including Random Forest and state vector machine (SVM), by effectively minimizing loss in complex spectral zones. Methodological Benchmarking demonstrated that the proposed DBSCAN-XGBoost framework significantly surpassed both traditional NDWI thresholding and pixel-based XGBoost classification, achieving a superior overall accuracy of 97% and an F1-score of 0.96. Feature importance analysis via the Gain metric underscored the synergistic role of mean NDWI and mean NIR in refining decision boundaries. By shifting to an object-oriented paradigm, the proposed model substantially reduced “salt-and-pepper” noise and partially mitigated topographic shadow errors. This research presents a promising object-based framework that, after further multi-site validation, could support high-fidelity water auditing across diverse Indian landscapes.

Accurate and rapid flood inundation mapping is critical for mitigating socio-economic and ecological disruptions, particularly in flood-prone regions like India. This study presents an advanced framework leveraging EOS-04 C-band SAR data, integrating multi-parameter analysis – including the normalized difference flood index (NDFI), temporal backscatter variability (standard deviation (SD)), and dual-polarization (HH/HV) metrics – to enhance detection accuracy in complex environments. Five machine learning models were rigorously evaluated across diverse hydrological basins, with random forest emerging as optimal (98.8% accuracy, 95.7% recall), processing flood layers in 85 s – a benchmark for rapid response. A user-friendly graphical user interface (GUI) was developed to operationalize near-real-time mapping, validated against optical satellite data (94% agreement), and deployed in high-risk zones such as the Godavari-Sabari, Ganga, and Brahmaputra basins. Key innovations include the first fusion of NDFI with temporal SD to distinguish dynamic flood signals from permanent water bodies. The framework's computational efficiency and open-source adaptability position it as a scalable tool for disaster agencies. By bridging gaps in real-time SAR analytics, this work advances flood resilience strategies, offering a template for adaptive risk management in monsoonal and climate-vulnerable regions globally.