An autoencoder driven deep learning geospatial approach to flood vulnerability analysis in the upper and middle basin of river Damodar

Abstract Flood vulnerability mapping has significantly progressed with the advent of Machine Learning (ML), bringing greater certainty to predictions. However, conventional supervised ML techniques may not be feasible in regions where recorded flood inventory data is scarce. This study introduces a...

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Principais autores:	Rohit Srinivas Thappitla, Vasanta Govind Kumar Villuri, Satish Kumar
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Nature Portfolio 2025-09-01
coleção:	Scientific Reports
Assuntos:	Flood vulnerability Deep learning Unsupervised learning Autoencoder Remote sensing and geographic information systems Damodar basin
Acesso em linha:	https://doi.org/10.1038/s41598-025-96781-2
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