Assessing Coastal Flood Susceptibility in East Java, Indonesia: Comparison of Statistical Bivariate and Machine Learning Techniques

Floods in coastal areas occur yearly in Indonesia, resulting in socio-economic losses. The availability of flood susceptibility maps is essential for flood mitigation. This study aimed to explore four different types of models, namely, frequency ratio (FR), weight of evidence (WofE), random forest (...

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書誌詳細
出版年:Water (Switzerland)
第一著者: 2-s2.0-85143664082
フォーマット: 論文
言語:English
出版事項: MDPI 2022
オンライン・アクセス:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143664082&doi=10.3390%2fw14233869&partnerID=40&md5=04ff00cb4d1992acd91667cd0f532025
その他の書誌記述
要約:Floods in coastal areas occur yearly in Indonesia, resulting in socio-economic losses. The availability of flood susceptibility maps is essential for flood mitigation. This study aimed to explore four different types of models, namely, frequency ratio (FR), weight of evidence (WofE), random forest (RF), and multi-layer perceptron (MLP), for coastal flood susceptibility assessment in Pasuruan and Probolinggo in the East Java region. Factors were selected based on multi-collinearity and the information gain ratio to build flood susceptibility maps in small watersheds. The comprehensive exploration result showed that seven of the eleven factors, namely, elevation, geology, soil type, land use, rainfall, RD, and TWI, influenced the coastal flood susceptibility. The MLP outperformed the other three models, with an accuracy of 0.977. Assessing flood susceptibility with those four methods can guide flood mitigation management. © 2022 by the authors.
ISSN:20734441
DOI:10.3390/w14233869