Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupl...
Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupling model: a Hangzhou city case study
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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As the demand for regional geological disaster risk assessments in large cities continues to rise, our study selected Hangzhou, one of China’s megacities, as a model to evaluate the susceptibility to two major geological hazards in the region: ground collapse and ground subsidence. Given that susceptibility assessments for such disasters mainly rel...
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Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupling model: a Hangzhou city case study
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TN_cdi_doaj_primary_oai_doaj_org_article_8f31e4fa1f3345adb6ea4cfbc941440a
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_8f31e4fa1f3345adb6ea4cfbc941440a
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2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-024-71053-7
