Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
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; the Kelantan River basin in Malaysia, which needs further processing for ML_V. The results showed that the precision of the forecasted map was around 80% which is compared with another Gaussian Naïve Bias ML algorithm. However, in imbrication with the ArcGIS maps, the forecasted map detached the “out of boundary” images and generated a clearer map. It is obvious that this ML_V model was anticipated to read the final output, which is involved during the flood guidance statement (FGS) to broadcast the data to the community.
Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
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; the Kelantan River basin in Malaysia, which needs further processing for ML_V. The results showed that the precision of the forecasted map was around 80% which is compared with another Gaussian Naïve Bias ML algorithm. However, in imbrication with the ArcGIS maps, the forecasted map detached the “out of boundary” images and generated a clearer map. It is obvious that this ML_V model was anticipated to read the final output, which is involved during the flood guidance statement (FGS) to broadcast the data to the community.
Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
Water Res.Develop.Managem.
Mohd Sidek, Lariyah (Herausgeber:in) / Salih, Gasim Hayder Ahmed (Herausgeber:in) / Ahmed, Ali Najah (Herausgeber:in) / Escuder-Bueno, Ignacio (Herausgeber:in) / Basri, Hidayah (Herausgeber:in) / Sidek, Lariyah Mohd (Autor:in) / Basri, Hidayah (Autor:in) / Marufuzzaman, Mohd (Autor:in) / Deros, Atiqah Mohd (Autor:in) / Osman, Sazali (Autor:in)
International Conference on Dam Safety Management and Engineering ; 2023 ; Kuala Lumpur, Malaysia
05.02.2024
16 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Flood forecasting , Machine learning , Decision tree , Flood hazard map , Kelantan , Risk assessment , Flood impacts Environment , Environmental Management , Water, general , Security Science and Technology , Risk Management , Environmental Economics , Pollution, general , Earth and Environmental Science
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