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 occu...

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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
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Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-34400
record_format	dspace
spelling	my.uniten.dspace-344002024-10-14T11:19:31Z Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins Sidek L.M. Basri H. Marufuzzaman M. Deros A.M. Osman S. Hassan F.A. 35070506500 57065823300 56976224000 58905646500 57189233135 58905990100 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 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 Na�ve 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. � The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. Final 2024-10-14T03:19:31Z 2024-10-14T03:19:31Z 2023 Book chapter 10.1007/978-981-99-3708-0_1 2-s2.0-85185928772 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185928772&doi=10.1007%2f978-981-99-3708-0_1&partnerID=40&md5=f571c5c84e911847efe0984486cca593 https://irepository.uniten.edu.my/handle/123456789/34400 Part F2265 1 16 Springer Science and Business Media Deutschland GmbH Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Decision tree Flood forecasting Flood hazard map Flood impacts Kelantan Machine learning Risk assessment
spellingShingle	Decision tree Flood forecasting Flood hazard map Flood impacts Kelantan Machine learning Risk assessment Sidek L.M. Basri H. Marufuzzaman M. Deros A.M. Osman S. Hassan F.A. Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
description	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
author2	35070506500
author_facet	35070506500 Sidek L.M. Basri H. Marufuzzaman M. Deros A.M. Osman S. Hassan F.A.
format	Book chapter
author	Sidek L.M. Basri H. Marufuzzaman M. Deros A.M. Osman S. Hassan F.A.
author_sort	Sidek L.M.
title	Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
title_short	Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
title_full	Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
title_fullStr	Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
title_full_unstemmed	Real-Time Flood Inundation Map Generation Using Decision Tree Machine Learning Method: Case Study of Kelantan River Basins
title_sort	real-time flood inundation map generation using decision tree machine learning method: case study of kelantan river basins
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2024
_version_	1814061178456899584

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

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