Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?

[Display omitted] •A new robustness deep learning model, DEBP, was developed for flood susceptibility mapping.•The IGR feature selection was applied to determine the most important flood conditioning factors.•The proposed model was optimized by PSO to enhance the goodness-of-fit and prediction accur...

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Veröffentlicht in:	Journal of hydrology (Amsterdam) Jg. 592; S. 125615
Hauptverfasser:	Pham, Binh Thai, Luu, Chinh, Phong, Tran Van, Trinh, Phan Trong, Shirzadi, Ataollah, Renoud, Somayeh, Asadi, Shahrokh, Le, Hiep Van, von Meding, Jason, Clague, John J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2021
Schlagworte:	algorithms decision support systems Deep belief network Extreme machine learning Flood susceptibility Goodness-of-fit logit analysis neural networks Optimization algorithm prediction trees Vietnam watersheds Vietnam Optimization algorithm Vietnam Deep belief network Goodness-of-fit Flood susceptibility Extreme machine learning
ISSN:	0022-1694, 1879-2707
Online-Zugang:	Volltext
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