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

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According to the high-resolution typhoon loss zoning map of rural housing provided in this paper, local emergency management departments can carry out timely and targeted reinforcement and repair of rural housing in key townships before the typhoon comes to minimize the casualties and economic losses caused by wind collapse.

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.

1. Introduction

Ningbo is located in the southeast coast of China and is seriously affected by typhoon disasters every year. For example, the Super Typhoon Lekima hit Ningbo in 2019, causing 408 houses to collapse and damage to 1594 houses citywide. Compared with urban houses, rural houses are less wind-resistant, especially some old houses in disrepair, which are highly susceptible to typhoon damage. Among the existing rural houses in Ningbo, only 14.8% are reinforced concrete houses, 69.9% are brick and concrete houses, and 14.9%, 0.1%, and 0.3% are brick (stone) wooden houses, bamboo and grass adobe houses, and other structures, respectively, according to the data of the third agricultural census in Ningbo (http://www.cnnb.com.cn/xinwen/system/2018/03/05/008731132.shtml, accessed on 1 May 2021). Except for the reinforced concrete structure, which has better wind resistance, 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 Batts et al. [1], Georgiou [2], Vickery and Twisdale [3], Ou et al. [4], Zhao et al. [5], ** transformation on the data through the basis function. The output layer uses a linear optimization strategy, so the learning speed can be faster. Finally, the mapped values are output by a linearly weighted combination of the output layer neural nodes.

The input layer of RBF neural network realizes the spatial nonlinear map** transformation $x_p\to R(x_p-c_i)$ by a radial basis function, where the commonly used basis functions are Gaussian functions with the following activation functions:

$$R(x_p-c_i)=\exp (-\frac{1}{2{\sigma }^2}‖x_p-{c_i‖}^2)$$

(1)

where $‖x_p-c_i‖$ is the Euclidean norm; $x_p={\left(x_1^p,x_2^p,\dots ,x_m^p\right)}^{\mathrm{T}}$ representing the pth input data; c_i is the implicit layer node center; and σ is the variance of the basis function.

The output layer of the RBF neural network achieves a linear map** $R(x_p-c_i)\to y_j$ by weighted combinations, denoted as:

$$y_j={\displaystyle {\sum }_{i=1}^h{\omega }_{ij}}R(x_p-c_i)\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}j=1,2,\dots ,n$$

(2)

where ${\omega }_{ij}$ is the connection weight from the implicit layer to the output layer; $i=1,2,3,\dots ,h$, h is the number of nodes in the hidden layer; and $y_j$ is the jth actual output value corresponding to the data.

3.2. BP Neural Network Fundamentals

The BP (back propagation) neural network is a multilayer feedforward network trained according to the error back propagation algorithm and is one of the most widely used neural network models, whose network structure is shown in Figure 3. The standard BP network generally consists of three neuron layers: the input layer, the hidden layer, and the output layer, and the neurons in the layers form a fully interactive connection with each other, and the neurons within the layers are independent of each other. The BP neural network algorithm is based on the error between the true value and the output value, and adjusts its weights and threshold values backward to finally achieve the minimum mean square error. Since the learning speed of BP neural network is fixed, the network converges slowly and requires a long training time.

3.3. Construction of Typhoon Loss Assessment Model for Rural Housing

The flow chart for the construction of a typhoon loss assessment model for rural housing is shown in Figure 4. As can be seen, the simulated typhoon wind speed was selected as input layer data, and the output layer data were the insured loss rate of each township due to typhoons, and the statistical data covered 139 townships in Ningbo. Based on the available data of the typhoon causal factors and the losses of the disaster-bearing bodies, the typhoon loss assessment models of rural housing based on the RBF neural network and BP neural network can be established, respectively. Among them, the construction of the RBF neural network is simpler than a BP neural network. Based on the existing data samples, a neural network with a radial basis function expansion factor of 1.2 was created by the “newrb” function, and the mean square error target was set to 0.001. The training algorithm of RBF neural network can be divided into two steps: the first step is to decide the number of nodes in the hidden layer and the center c_i of the basis function according to the distribution of training samples; and the second step is to obtain the connection weights ${\omega }_{ij}$ based on the determined network parameters.

The BP neural network was designed as a typical 3-layer network model. The number of neurons in the hidden layer was determined as five by the empirical formula and trial-and-error method, and the transfer functions were tangent S-type transfer function “tansig” and linear transfer function “purelin”, respectively. The system default “trainlm” function was used for training, and the training number was set to 1000, the training target was 0.001, and other parameters were taken as default values.