# 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. typhoon; RBF neural network; township-level resolution; loss assessment +
Footnote †: journal: selected topics  
houses of brick and concrete, brick and stone, bamboo and grass structures may collapse due to typhoons. Therefore, it is increasingly important to make reasonable disaster loss assessments and predictions for rural housing. Many scholars in China and abroad have conducted a lot of research on the hazard analysis of typhoon causal factors and vulnerability analysis of disaster-bearing bodies. In the assessment of typhoon causal factors, benefited from the rapid growth of the Monte Carlo method and computer technology, extreme typhoon wind speed simulation has mushroomed in the past twenty years, which is mainly composed of two parts: (1) typhoon track simulation, and (2) wind field simulation. There are two commonly used methods, the circular sub-region method (CSM) and full-track method, which were put forward one after another to serve the typhoon track simulation. Studies in the literature that have indicated the use of CSM to assess the typhoon wind hazards for the coastal region of mainland China or the hurricane wind hazards for the United States have been carried out by [PERSON] et al. [1], [PERSON] [2], [PERSON] and [PERSON] [3], [PERSON] et al. [4], [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] and [PERSON] et al. [7], [PERSON] et al. [8], and [PERSON] et al. [9]. This approach (referred to as the CSM) requires the assignment of probabilistic models for the characteristics of historical track segments within a specified radius of the site of interest such as translational speed, heading, and intensity. However, there could be insufficient historical track data to adequately define the probabilistic models in this approach. To overcome the lack of historical track data, [PERSON] et al. [10] pioneered a full-track method to generate synthetic hurricane tracks from genesis to lysis based on the historical track records in the National Hurricane Center's North Atlantic hurricane database (HURDAT). The development and utilization of the full-track models for wind hazard assessment have since been considered and expanded ([PERSON] et al. [11]; [PERSON] and [PERSON] [12]; [PERSON] et al. [13]; [PERSON] and [PERSON] [14]; [PERSON] et al. [15]; [PERSON] and [PERSON] [16; 17]; [PERSON] and [PERSON] [18]). [PERSON] and [PERSON] [16; 17] simplified the [PERSON]'s regression model to assess the hurricane/ typhoon wind hazards on the U.S. gulf coast and the coastal region of mainland China, respectively.