Research on Applied Sciences Detailed by Researchers at School of Civil Engineering and Architecture (Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution): Science – Applied Sciences

2022 APR 22 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Fresh data on applied sciences are presented in a new report. According to news reporting from Ningbo, People’s Republic of China, by NewsRx journalists, research stated, “The purpose of this paper was to provide a new approach to achieve quantitative and accurate typhoon loss assessment of disaster-bearing bodies at township-level resolution.”

Funders for this research include Zhejiang Provincial Natural Science Foundation Funder DOI: 10.13039/501100004731 Award: LQ20E080001 Science and Technology Special Project of Ningbo Fenghua District Award: 202008502.

The news journalists obtained a quote from the research from School of Civil Engineering and Architecture: “Based on the policy insurance data of Ningbo city, this paper took rural housing as the target disaster-bearing body and analyzed the aggregated data of disaster losses such as payout amount and insured loss rate of rural housing in Ningbo area under the influence of 25 typhoons during 2014-2019. The intensity data of disaster-causing factors such as the maximum average wind speed in Ningbo area under the influence of 25 typhoons were simulated and generated with the wind field engineering model, and a township-level high-resolution rural housing typhoon loss assessment model was established using a RBF artificial neural network. It was found that the insured loss rate of rural housing under wind damage was higher in the townships of southern Ningbo than in the townships of northern Ningbo, and the townships with larger insured loss rates were concentrated in mountainous or coastal areas that are prone to secondary disasters under the attack of the typhoon’s peripheral spiral wind and rain belt.”

According to the news reporters, the research concluded: “The RBF neural network can effectively establish a typhoon loss assessment model from the causal factors to the losses of the disaster-bearing bodies, and the RBF neural network has a faster convergence speed and a smaller overall prediction error than the commonly used BP neural network.”

For more information on this research see: Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution. Applied Sciences, 2022,12(3463):3463. (Applied Sciences - http://www.mdpi.com/journal/applsci). The publisher for Applied Sciences is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/app12073463.

Our news editors report that additional information may be obtained by contacting Qiang Li, School of Civil Engineering and Architecture, NingboTech University, Ningbo 315100, People’s Republic of China. Additional authors for this research include Hongtao Jia, Jun Zhang, Jianghong Mao, Weijie Fan, Mingfeng Huang, Bo Zheng.

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