Simulating flood risk in Tampa Bay using a machine learning driven approach

The study’s goal is to simulate flood risk and identify dominant flood risk factors (FRFs) in Tampa Bay, Florida using historical flood damage data as target variable, with 16 FRFs as predictor variables. Five different machine learning (ML) models such as decision tree (DT), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and random forest (RF) were adopted.

RF classifies 2.42% of Tampa Bay as very high risk and 2.54% as high risk, while XGBoost classifies 3.85% as very high risk and 1.11% as high risk. Moreover, the communities reside at low altitudes and near the waterbodies, with dense man-made infrastructure, are at high flood risk. This study introduces a comprehensive framework for flood risk assessment and helps policymakers mitigate flood risk.