Reports from University of Tehran Provide New Insights into Flood Risk Management (Flood Susceptibility Mapping Using Support Vector Regression and Hyper-parameter Optimization): Risk Management - Flood Risk Management - Insurance News | InsuranceNewsNet

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June 27, 2023 Newswires
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Reports from University of Tehran Provide New Insights into Flood Risk Management (Flood Susceptibility Mapping Using Support Vector Regression and Hyper-parameter Optimization): Risk Management – Flood Risk Management

Insurance Daily News

2023 JUN 27 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Current study results on Risk Management - Flood Risk Management have been published. According to news originating from Karaj, Iran, by NewsRx correspondents, research stated, “Floods are both complex and destructive, and in most parts of the world cause injury, death, loss of agricultural land, and social disruption. Flood susceptibility (FS) maps are used by land-use managers and land owners to identify areas that are at risk from flooding and to plan accordingly.”

Our news journalists obtained a quote from the research from the University of Tehran, “This study uses machine learning ensembles to produce objective and reliable FS maps for the Haraz watershed in northern Iran. Specifically, we test the ability of the support vector regression (SVR), together with linear kernel (LK), base classifier (BC), and hyper-parameter optimization (HPO), to identify flood-prone areas in this watershed. We prepared a map of 201 past floods to predict future floods. Of the 201 flood events, 151 (75%) were used for modeling and 50 (25%) were used for validation. Based on the relevant literature and our field survey of the study area, 10 effective factors were selected and prepared for flood zoning. The results show that three of the 10 factors are most important for predicting flood-sensitive areas, specifically and in order of importance, slope, distance to the river and river.”

According to the news editors, the research concluded: “Additionally, the SVR-HPO model, with area under the curve values of 0.986 and 0.951 for the training and testing phases, outperformed the other two tested models.”

For more information on this research see: Flood Susceptibility Mapping Using Support Vector Regression and Hyper-parameter Optimization. Journal of Flood Risk Management, 2023. Journal of Flood Risk Management can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - http://www.wiley.com/; Journal of Flood Risk Management - http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1753-318X)

The news correspondents report that additional information may be obtained from Aryan Salvati, University of Tehran, Faculty of Natural Resources, Dept Arid & Mt Reg Reclamat, Karaj, Iran. Additional authors for this research include Alireza Moghaddam Nia, Ali Salajegheh, Kayvan Ghaderi, Dawood Talebpour Asl, Nadhir Al-Ansari, Feridon Solaimani and John J. Clague.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1111/jfr3.12920. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

(Our reports deliver fact-based news of research and discoveries from around the world.)

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