Flood Prediction Map Using QGIS and ML-Case Study for Barpeta District Assam

Across the world, floods pose a serious threat to populations, hence precise forecasting techniques required for efficient relief. The objective of this research is to forecast floods in the Barpeta district of Assam, India, by combining machine learning approaches with Geographic Information Systems (GIS). Through the integration of many geographic datasets, such as river networks, administrative boundaries, and Digital Elevation Models (DEMs), in conjunction with rainfall data and a 2022 flood hazard map, the study creates prediction models to identify flood-prone regions and assess flood risk levels. Utilizing the XGBoost and GBM algorithm, the study assesses model performance and visualizes results using GIS software. The results offer significant perspectives to decision-makers and interested parties, supporting the development of plans to lessen the effects of flooding in areas that are susceptible to flooding. Through a data-driven approach, this research highlight the potential of integrating GIS and machine learning techniques to address complex environmental challenges and promote resilience in flood-prone areas.