- English
Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each
rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize
flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of
machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter
suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility
zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random
Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is
vital in situations where conventional numerical models face challenges due to limited data, such as river
discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence
of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman’s rho
test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression
for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given
its strong performance and effective training datasets, is recommended for areas with similar physical characteristics.
The district-wise priority map generated from the weighted results of flood susceptibility assessments
will be useful for flood management and mitigation strategies. Additionally, the study found that applying the
AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of
the flood management process. This research offers valuable insights for authorities to better address flooding
and improve flood prevention and management efforts in floodplain regions, contributing to broader climate
change adaptation strategies.
- English
