Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins

• Main Authors: Sidek L.M., Basri H., Marufuzzaman M., Deros A.M., Osman S., Hassan F.A.
• Other Authors: 35070506500
• Format: Book chapter
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Subjects: Decision tree; Flood forecasting; Flood hazard map; Flood impacts; Kelantan; Machine learning; Risk assessment

Spatial investigation and rainfall affect the most in runoff and flood modelling compared to other influencing flood sources. Therefore, flash flood and short-term flood prediction require numerical rainfall estimation, which employs falls, mudflow, melted ice, etc. To forecast unexpected flood occurrences, faster flood prediction necessitates computational prediction models such as Machine Learning (ML) algorithms, which are extensively utilized around the world. So, in this research, a real-time flood inundation map (FIM) is used to develop the ML-based visualization (ML_V) method to characterize the collected dataset. Additionally, to predict the flood depth, a trained Decision Tree (DT)-based sorting algorithm is used in this method. This DT-based model takes 4 random rainfall data to train and predict the flood depth of the study area