Enhancing precision flood mapping: Pahang's vulnerability unveiled v1

Flooding in Malaysia is considered one of the most impactful natural disasters. Annually, Pahang experiences substantial destruction due to floods. The aim of this research is to address the urgent issue of flood susceptibility in Pahang, Malaysia. To achieve this, a combination of Geographic Information System (GIS) and Ensemble Machine Learning (EML) will be utilized. By considering nine factors from a geospatial database that contribute to flooding, the areas prone to floods will be mapped. The mapping process will be carried out using the ArcGIS environment, and a model called Random Forest (RF)-embedding will be developed using the Ensemble Machine Learning (EML) technique. To determine the most influential factors in flooding, Feature Selection (FS) will be employed. The accuracy of the flood susceptibility models will be assessed by analysing the Area Under the Curve (AUC). Flood susceptibility mapping is a complex procedure with uncertainties. Hence, our research can contribute to flood management in vulnerable regions by improving flood models and providing spatial outcomes to help decision-makers implement risk reduction strategies.