Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution

Abstract

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. 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. 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.