Forecasting the impact of land utilization on flood vulnerability through machine learning and remote sensing in Athuraliya and Akuressa divisional secretariat

Abstract

Akuressa and Athuraliya Divisional Secretariats in Sri Lanka frequently experience severe damage to human lives, infrastructure, and economic growth due to floods. These floods are often caused by elements like land use patterns, urbanization, and environmental degradation. This study aims to establish the connection between flood vulnerability and land use as importantly necessary for effective disaster management and mitigation strategies. Therefore, this research provides useful knowledge on flood vulnerability prediction based on land use patterns that can be used by policymakers, urban planners, and disaster management authorities for decision-making on proactive measures that will reduce the negative impacts caused by flooding while building resilience in the region. The primary purpose of this research is its innovative and essential because no earlier study has applied these cutting-edge techniques to assess flood risks in this area. Consequently, there is a significant gap in the current knowledge base and practice. Therefore, this research is intended to understand the land utilization situation in the area and how it affects flood vulnerability, identify environmental key variables that contribute to flood susceptibility, and use machine learning models including XGBoost, Random Forest, and CatBoost for predicting flood susceptibility. The latter also uses DEM derived factors with geological, soil, land use/land cover data, distance from roads and rivers to provide a closer understanding of flood conditioning factors within the study area. The XGBoost algorithm gave an accuracy score of 0.91 throughout the other utilized Machine Learning models, confirming how well machine learning performs when it comes to predictions. The results from the machine learning model were then used to determine the feature importance according to each conditioning factor that influences floods. Based on these feature importance values; a future risk map was generated using ArcGIS software. Therefore, this research indicates that prediction-based planning is more effective than post event-based recovery measures in building resilient and sustainable communities prone to flooding like Akuressa and Athuraliya Divisional Secretariat. In addition, these findings show that Machine Learning (ML) and Remote Sensing (RS) have potential for improving on-flood forecasting techniques as well as mitigating measures.