Flood-prone area mapping using a synergistic approach with swarm intelligence and gradient boosting algorithms

Accurate flood susceptibility mapping (FSM) is a control approach to flood management. This research introduces a novel approach to increase the accuracy of FSM by optimizing the CatBoost algorithm with two swarm-based metaheuristic algorithms: the Zebra optimization algorithm (ZOA) and the Whale op...

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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 27924 - 20
Hlavní autoři:	Razavi-Termeh, Seyed Vahid, Sadeghi-Niaraki, Abolghasem, Abba, Sani I., Hussain, Jamil, Choi, Soo-Mi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 31.07.2025 Nature Publishing Group Nature Portfolio
Témata:	704/242 704/4111 Accuracy Algorithms Artificial intelligence Boosting algorithm Flood control Flood management Flood-prone area mapping Floods Geographic information systems Humanities and Social Sciences Infrastructure Learning algorithms Machine learning Mapping multidisciplinary Neural networks Optimization algorithms Science Science (multidisciplinary) Semi-arid climate Storm damage Support vector machines Swarm-based metaheuristics Topography Wind Middle East Iran Semi-arid climate Flood management Flood-prone area mapping Boosting algorithm Swarm-based metaheuristics
ISSN:	2045-2322, 2045-2322
On-line přístup:	Získat plný text
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