Articles | Volume 24, issue 12
https://doi.org/10.5194/nhess-24-4385-2024
https://doi.org/10.5194/nhess-24-4385-2024
Research article
 | 
05 Dec 2024
Research article |  | 05 Dec 2024

Integrating susceptibility maps of multiple hazards and building exposure distribution: a case study of wildfires and floods for the province of Quang Nam, Vietnam

Chinh Luu, Giuseppe Forino, Lynda Yorke, Hang Ha, Quynh Duy Bui, Hanh Hong Tran, Dinh Quoc Nguyen, Hieu Cong Duong, and Matthieu Kervyn

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Cited articles

Abedi Gheshlaghi, H., Feizizadeh, B., Blaschke, T., Lakes, T., and Tajbar, S.: Forest fire susceptibility modeling using hybrid approaches, T. GIS, 25, 311–333, https://doi.org/10.1111/tgis.12688, 2021. 
Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J. R., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., and Boer, M. M.: Connections of climate change and variability to large and extreme forest fires in southeast Australia, Commun. Earth Environ., 2, 8, https://doi.org/10.1038/s43247-020-00065-8, 2021. 
Adab, H., Kanniah, K. D., and Solaimani, K.: Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques, Nat. Hazards, 65, 1723–1743, https://doi.org/10.1007/s11069-012-0450-8, 2013. 
Agus, C., Ilfana, Z., Azmi, F., Rachmanadi, D., Widiyatno, Wulandari, D., Santosa, P., Harun, M., Yuwati, T., and Lestari, T.: The effect of tropical peat land-use changes on plant diversity and soil properties, Int. J. Environ. Sci. Te., 17, 1703–1712, https://doi.org/10.1007/s13762-019-02579-x, 2020. 
Ahmadlou, M., Karimi, M., Alizadeh, S., Shirzadi, A., Parvinnejhad, D., Shahabi, H., and Panahi, M.: Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA), Geocarto Int., 34, 1252–1272, https://doi.org/10.1080/10106049.2018.1474276, 2018. 
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Short summary
This study presents a novel and integrated approach to assessing the climate hazards of floods and wildfires. We explore multi-hazard assessment and risk through a machine learning modeling approach. The process includes collecting a database of topography, climate, geology, environment, and building data; developing models for multi-hazard assessment and coding in the Google Earth Engine; and producing credible multi-hazard susceptibility and building exposure maps.
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