Tree lodging risk assessment method and device based on disaster risk theory

Abstract

The invention relates to the technical field of disaster assessment, and discloses a tree lodging risk assessment method and device based on a disaster risk theory. The assessment method comprises the following steps: acquiring a basic data set for disaster risk assessment; and inputting the basic data into a pre-constructed tree lodging risk index calculation model, and calculating and outputting a predicted tree lodging risk index. According to the embodiment of the invention, the assessment precision is improved: real-time and future predicted risk reminding is carried out by establishing a quantitative tree lodging risk index, and the precision and accuracy of disaster risk assessment are improved; overall-process risk prompting is realized; overall-process dynamic risk prompting is carried out around historical, real-time and future key time points, so that the disaster response efficiency is improved; and through more accurate disaster assessment and early warning, the landscaping maintenance cost and loss are reduced, and the social benefit of disaster response is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster assessment, and in particular to a tree falling risk assessment method and device based on disaster risk theory. Background Art

[0002] With global climate change, the frequency and intensity of typhoon disasters are on the rise, and the management and protection of trees and urban greening have become particularly important. By studying the causes and mechanisms of typhoon tree fall and the wind risk impact forecast, we can provide a scientific basis for urban greening projects and reduce the damage caused by natural disasters such as typhoons to urban greening.

[0003] The current typhoon warning and forecast focuses on the forecast of typhoon wind and rain elements, but there is a lack of quantitative risk forecast products for natural disasters caused by typhoons, especially the risk products for tree falling caused by typhoons have not yet been developed. Since Typhoon Hato in 2017 and Typhoon Mangkhut in 2018, urban landscaping in Shenzhen has suffered huge economic losses during typhoons, which has also affected the safety of citizens' lives. By studying the causes and mechanisms of typhoon tree falling, effective prevention and response measures can be taken to reduce the impact of typhoons on urban greening management. Summary of the invention

[0004] The purpose of the present invention is to provide F so as to take effective preventive and countermeasure measures to reduce the impact of typhoons on urban greening management.

[0005] To achieve this object, the present invention adopts the following technical solutions: A tree fall risk assessment method based on disaster risk theory, including: Access to basic data sets for disaster risk assessment; The basic data is input into a pre-built tree lodging risk index calculation model to calculate and output a predicted tree lodging risk index.

[0006] Optionally, it also includes: establishing and preserving a typhoon disaster case database, including the distribution of strong winds in the city, the distribution of fallen trees, and / or the risk index of falling trees, to provide a reference for future disaster loss estimates.

[0007] Optionally, it also includes: combining disaster thresholds and tree risk areas to conduct comprehensive disaster rating and comprehensive disaster loss estimation.

[0008] Optionally, the basic data set at least includes: Wind and rainfall data for both actual weather conditions and grid-based forecasts; The planting situation of roadside trees on urban main roads, including at least one of the following: tree number, road section, location, tree species, coordinates, family name, genus name, tree age, height, and diameter at breast height; and / or, city-wide road data and historical disaster data.

[0009] Optionally, the tree fall risk index TR is calculated based on the road gust index TH, the tree fall vulnerability TV, the tree exposure TE, and the soil water content SM.

[0010] Optionally, the tree fall risk index TR is specifically calculated according to the formula TR = TH * TV * TE * SM.

[0011] Optionally, the tree fall vulnerability TV is calculated according to the following formula: TV = .

[0012] Optionally, it further includes: after calculating the tree fall risk index TR, performing hierarchical reminder according to the tree fall risk index TR.

[0013] Optionally, the method of the hierarchical reminder includes: When TR < 0.4, it is determined as low risk, and the tree fall ratio is low; When 0.4 < TR < 0.5, it is determined as medium risk, and the tree fall is average; When 0.5 < TR < 0.6, it is determined as high risk, and the tree fall ratio is relatively high; When TR > 0.6, it is determined as extremely high risk, and the tree fall ratio is extremely high.

[0014] A tree fall risk assessment device based on the disaster risk theory includes a memory and a processor; The memory is used for storing instructions; The processor is used for executing the instructions in the memory to implement the tree fall risk assessment method based on the disaster risk theory described in any one of the above.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1) Improve the assessment accuracy: By establishing a quantitative tree fall risk index and carrying out real-time and future prediction risk reminders, the accuracy and precision of the disaster risk assessment are improved.

[0016] 2) Realize the risk prompt of the whole process: Carry out dynamic risk reminders for the "whole process" around the key time points of history, live conditions, and the future, and improve the efficiency of disaster response.

[0017] 3) Social and economic benefits: Through more accurate disaster assessment and early warning, reduce the maintenance cost and loss of landscaping, and improve the social benefits of disaster response. Description of the Drawings

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic diagram of a tree falling risk assessment method based on disaster risk theory provided by an embodiment of the present invention; Figure 2 A schematic diagram of tree falling risk prediction in Shenzhen provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In order to overcome the problems of the prior art, Figure 1 and Figure 2 As shown, the embodiment of the present invention proposes a tree falling risk assessment method based on disaster risk theory, comprising the following steps: S1: Data acquisition.

[0022] Obtain basic data sets for disaster risk assessment, including wind and rainfall data of actual weather conditions and grid forecasts, roadside tree planting conditions on urban main roads, including tree number, road section, location, tree species, coordinates, family name, genus name, tree age, height, diameter at breast height, etc.; city-wide road data and historical disaster data.

[0023] S2: Data input.

[0024] The basic data is input into a tree lodging risk index calculation model.

[0025] S3: Index calculation.

[0026] Output the actual + predicted tree fall risk index from the S2 calculation model.

[0027] S4: Establish a case library.

[0028] Establish and preserve a typhoon disaster case database, including the distribution of strong winds throughout the city, the distribution of fallen trees, and the risk index of falling trees, to provide a reference for future disaster loss estimates.

[0029] S5: Comprehensive risk index estimates the tree lodging rate. Combined with the disaster threshold and tree risk area, comprehensive disaster rating and comprehensive disaster loss estimation are carried out.

[0030] In this patent, we proposed a new method for estimating the risk of tree falling. We established a tree falling risk index system and a historical case database, taking into account the intensity, duration, rainfall and soil moisture of gusts during typhoon weather processes, the fragility and exposure of trees, the impact range of historical typhoons, disaster conditions and other factors, and referring to relevant standards and business specifications to establish a tree falling risk index system.

[0031] (1) Application of disaster risk theory. According to the disaster risk formation principle and evaluation index system, the indicators of disaster hazard, exposure degree of disaster-prone body and vulnerability of disaster-prone body are normalized and weighted and integrated to establish the risk assessment model as follows: (1) In the formula, R is the disaster risk index, which is used to indicate the degree of disaster risk. The larger its value, the greater the degree of disaster risk. H, E, and V represent the disaster risk, the exposure of the disaster-bearing body, and the vulnerability index of the disaster-bearing body, respectively. In the actual situation of the tree falling risk index, the disaster risk is the disaster risk of meteorological factors, such as the risk of strong winds and rainfall, which can consider factors such as wind force and duration of strong winds, and rainfall size; the vulnerability of the disaster-bearing body is the characteristics of tree falling, such as growth characteristics such as tree age, height, and diameter at breast height. The exposure of the disaster-bearing body is the number of disaster-bearing bodies, that is, the number of trees. Therefore, when designing risk warning products, the disaster risk and vulnerability factors are normalized according to formula (2) so that the result value range is [0,1].

[0032] (2) in, is the normalized data, is the sample data, is the maximum value in the sample data.

[0033] (2) Disaster risk caused by meteorological factors Spatial matching of tree-falling data in each street during the period of Typhoon Mangkhut with the distribution of the city's maximum gusts showed that the high-value areas of tree-falling basically coincided with the high-value areas of the maximum gusts. Considering the duration of tree-falling under the influence of strong winds and the size of gusts, the road gust index (TH) was established using the gust data of automatic stations near the road.

[0034] (3) in TH is the road gust index, V For gusts of wind, In daily operations, the maximum gust value is taken as 75m / s. T is the duration of the gust, The longest impact time of a typhoon during the period 2008-2016 was 3 days and 72 hours.

[0035] (3) Soil moisture index The fall of tree trunks depends on two factors: one is the actual stress that the trunk can withstand; the other is the adhesion of the roots in the soil. When the adhesion of the roots to the soil is less than the actual stress, the tree will fall. Soil moisture is an important factor in soil adhesion. The relationship between rainfall and tree fall was obtained by looking at the relationship between the tree fall rate, soil moisture and rainfall during the period of Typhoon Mangkhut.

[0036] During Typhoon Mangkhut, the wind index in Yantian District ranked second among all districts, but the reason why the trees in this area did not fall down seriously may be that the soil moisture content in this area is very low and the root system has a strong adhesion to the soil. The soil moisture content at the Shenzhen East OCT station has the highest correlation with rainfall, with a correlation coefficient of 0.62 for 20cm soil moisture and 0.72 for 40cm soil moisture. Therefore, the East OCT station was selected as the soil moisture research index station, and the relationship between soil moisture and rainfall was established, using rainfall as the soil moisture index (SM).

[0037] Table 1 Soil moisture index

[0038] (4) Tree falling vulnerability (TV) Differences in tree species are also one of the reasons for differences in wind resistance. Several trees planted in the same year on the same street were selected to compare the proportion of trees falling and branches broken during the typhoon. The tree with the highest falling rate was the Silk Cotton Tree, reaching 44.44%. The tree with the highest breaking rate was the Jacaranda, which was 60.0%, and its falling rate was 40.0%, second only to the Silk Cotton Tree. The proportion of broken branches of the Delonix regia was also high, reaching 55.56%, but the falling rate was 0. The falling rate of the Acer truncatum was low, but its branch breaking rate was high. The falling rate and branch breaking rate of Mahogany, Terminalia microphylla and Cotton microphylla were all low. Comparison of the tree falling rates in different years in each district shows that the tree falling in Luohu District was the most serious, followed by Dapeng New District. Although the comprehensive wind index of Luohu District was slightly lower than that of Dapeng New District during the period of Typhoon Mangkhut, as Luohu District is an old district, the urban roads are narrow and the distances between buildings are small, which makes it easy for extreme strong winds to form locally due to the "narrow channel effect". At the same time, due to the narrow roads, the level of green space is reduced, which is not conducive to the wind resistance of trees.

[0039] Table 2. Comparison of the proportion of fallen trees in different regions and years during the impact of Typhoon Mangkhut

[0040] The wind resistance of trees can be classified by integrating physical characteristics such as tree height, diameter at breast height (DBH), crown width, crown shape, straightness of the trunk, air-dry density, leaf layer condition, and root system condition. Using the tree倒伏 rate during the mangosteen period and characteristics such as the planting quantity, height, and DBH of the trees to conduct relevant analysis and establish a regression equation, the higher the tree倒伏 rate, the higher the vulnerability (TV).

[0041] TV =

[0042] (5)Tree exposure (TE) Tree exposure is directly proportional to the number of trees planted. The larger the number of trees planted, the higher the proportion of tree倒伏 under the same conditions. Therefore, exposure = number of trees planted / maximum number of trees planted, and the tree exposure (TE) is defined according to the range of the number of trees planted.

[0043] Table 3 Relationship between tree exposure and the number of trees planted

[0044] 3. Tree倒伏 risk index (TR) To sum up, substituting each influencing factor into Formula 1, the tree倒伏 risk index is calculated as follows: TR = road gust index (TH) * tree倒伏 vulnerability (TV) * tree exposure (TE) * soil water content (SM)(5) 4. Calculation results and applications According to the calculation formula of Formula (5), inputting the actual and predicted data, the actual and predicted tree倒伏 risk indexes can be calculated, and the index results are classified and reminded. When TR < 0.4, it is (relatively) low risk, the proportion of tree倒伏 is (relatively) low, and the risk is (relatively) low; when 0.4 < TR < 0.5, it is medium risk, and the tree倒伏 is average; when 0.5 < TR < 0.6, it is high risk, the proportion of tree倒伏 is relatively high, and when TR > 0.6, it is extremely high risk, and the proportion of tree倒伏 is extremely high.

[0045] Based on the same concept, the embodiment of the present invention provides a tree倒伏 risk assessment device based on the disaster risk theory. The device includes a memory and a processor. At least one instruction is stored in the memory, and at least one instruction is loaded and executed by the processor to implement the tree倒伏 risk assessment method provided by the embodiment of the present invention.

[0046] To sum up, the comprehensive meteorological disaster comprehensive risk assessment scheme based on the index matching case method of the present invention has the following beneficial effects: 1) Improving assessment accuracy: By establishing a quantitative tree-fall risk index and providing real-time and future forecast risk alerts, the accuracy and precision of disaster risk assessments are improved.

[0047] 2) Achieve risk warnings throughout the entire process: Provide dynamic risk warnings throughout the entire process, focusing on historical, current, and future key time points to improve disaster response efficiency.

[0048] 3) Socio-economic benefits: Through more accurate disaster assessment and early warning, the cost and loss of landscaping maintenance can be reduced, and the social benefits of disaster response can be improved.

[0049] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tree falling risk assessment method based on disaster risk theory, characterized in that: Including: Obtain a basic data set for disaster risk assessment; Input the basic data into a pre-constructed tree toppling risk index calculation model, and calculate and output the predicted tree toppling risk index.

2. The tree falling risk assessment method based on disaster risk theory according to claim 1 is characterized in that: It also includes: Establish and save a typhoon disaster case library, including the city's strong wind distribution, tree toppling distribution, and / or toppling risk index, to provide a reference for future disaster loss estimation.

3. The tree falling risk assessment method based on disaster risk theory according to claim 1 or 2, characterized in that: It also includes: Combine the disaster-causing threshold and the tree risk area to conduct comprehensive disaster grading and comprehensive disaster loss estimation.

4. The tree falling risk assessment method based on disaster risk theory according to claim 1 is characterized in that: The basic data set at least includes: Wind and rainfall data of meteorological实况 and grid forecasts; The planting situation of street trees on urban main roads, including at least one of tree number, road section, location, tree species, coordinates, family name, genus name, tree age, height, and diameter at breast height; And / or, the city's road data and historical disaster data.

5. The tree falling risk assessment method based on disaster risk theory according to claim 4 is characterized in that: Calculate the tree toppling risk index TR based on the road gust index TH, tree toppling vulnerability TV, tree exposure TE, and soil water content SM.

6. The tree falling risk assessment method based on disaster risk theory according to claim 5 is characterized in that: The tree toppling risk index TR is specifically calculated according to the formula TR = TH * TV * TE * SM.

7. The tree falling risk assessment method based on disaster risk theory according to claim 5 is characterized in that: The tree toppling vulnerability TV is calculated according to the following formula: TV= 。 8. The tree falling risk assessment method based on disaster risk theory according to claim 6 is characterized in that: It also includes: After calculating the tree toppling risk index TR, conduct hierarchical reminders based on the tree toppling risk index TR.

9. The tree falling risk assessment method based on disaster risk theory according to claim 8, characterized in that: The method of the hierarchical reminder includes: When TR < 0.4, it is determined as a low risk, and the tree toppling ratio is low; When 0.4 < TR < 0.5, it is determined as a medium risk, and the tree toppling is average; When 0.5 < TR < 0.6, it is determined as a high risk, and the tree toppling ratio is relatively high; When TR > 0.6, it is determined as an extremely high risk, and the tree toppling ratio is extremely high.

10. A tree falling risk assessment device based on disaster risk theory, characterized in that: Including a memory and a processor; The memory is used to store instructions; The processor is used to execute the instructions in the memory to implement the tree toppling risk assessment method based on the disaster risk theory described in any one of claims 1 to 9.

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