CN111401702B - Offshore traffic risk assessment method - Google Patents
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Abstract
The invention relates to an offshore traffic risk assessment method, which comprises the following steps: the first step, constructing an offshore traffic risk assessment index system, wherein the index system comprises three risk components: 1) Risk; 2) Frailty and exposivity; 3) Ability to alleviate; secondly, establishing an evaluation index space database; thirdly, calculating the index weight of each risk component; fourthly, generating a component weighted graph; fifth step, sea traffic risk assessment-calculating sea traffic risk index, and further dividing into 5 grades: very high, medium, low, very low. The invention analyzes the intrinsic driving factors of the sea traffic risk, increases the transparency of the sea traffic risk, and provides important technical support for reducing the possibility of sea accidents. The combination of the geospatial technology, the multi-criterion decision and the risk index provides a scientific offshore traffic risk assessment method, and overcomes the defects of an offshore traffic risk tool.
Description
Technical Field
The invention relates to an offshore traffic risk assessment method, in particular to an offshore traffic risk assessment method based on spatial multi-attribute decision.
Background
Globalization of economies has prompted rapid development of offshore trade. Due to the low cost nature of maritime, approximately 90% of the trade worldwide is by maritime transportation (Baksh, 2018). However, with the rapid increase in the size and number of vessels, the frequency of marine accidents is also increasing (Knap, 2017;
2017). The consequences of an accident can lead to serious life and property losses and damage to the marine environment (Heij, 2011; zhou, 2019). Accordingly, marine navigation safety is continually being focused on by maritime authorities, shipping industry and society, and underscores the importance of marine traffic risk assessment (Huang, 2019).
Regarding the risk of sea transportation, a number of qualitative and quantitative evaluations have been made. Typically, marine transportation risk assessment studies are based on maritime reporting and statistical methods (Zhang, 2019). For example, zhang et al evaluated the sea risk of the Yangtze river course based on a Bayesian network. Vander et al propose a new multi-level approach to assessing and predicting risk of marine transportation. Chai et al propose a quantitative risk assessment technique to identify the likelihood of a ship collision. Baksh et al propose a risk model to estimate the likelihood of a marine accident. Huang et al quantitatively calculated the stranded risk using the monte carlo method. These research methods can be used to assess long-term risk conditions of ships. However, the risk information cannot be spatially presented based on maritime reporting and statistical methods. To address this problem, wang et al analyzed the spatial variation of airway risk using fuzzy analytic hierarchy process. Zhang proposes a shipping risk assessment method based on grey correlation theory, and generates a shipping risk map. Although marine traffic risk assessment has made some progress in terms of spatial scale, most methods have limited standards used in the assessment process. In particular, there is currently no comprehensive analysis method that considers driving factors (hazards, vulnerabilities, expositions, and mitigation capabilities) of marine traffic risk to determine overall voyage risk. On a spatial scale, a good understanding of risk driving factors is crucial for the formulation of risk mitigation measures and policies (Rothlisberger, 2017). Spatial analysis can effectively mine the spatially distributed features of risk (Hoque, 2019). Although such methods are used in many applications, they have not found effective use in marine risk assessment (Vander, 2015). Clearly, a spatial approach must be developed to assess the risk of sailing of a ship in a marine environment.
Disclosure of Invention
The invention aims to solve the technical problems that: the method for evaluating the risk of the marine traffic is provided for overcoming the defects in the prior art. Compared with the traditional method, the method combines the geospatial technology, the multi-criterion decision and the risk index, and provides a scientific assessment method for offshore traffic risk assessment.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: the offshore traffic risk assessment method comprises the following steps:
the risk component contains 8 risk indicators: water depth, typhoon average wind pressure, typhoon frequency, strong wind frequency, strong wave frequency, daily average sea fog coverage, daily average rainfall and pirate frequency;
the fragile and exposed components contained 4 risk indicators: shortest coast distance, shortest port distance, shortest island reef distance, and ship density;
the remission ability component comprises 2 risk indicators: the search and rescue time and the shortest route distance;
step 2, establishing a risk index space database, namely downloading all original data related to the risk index, processing the original data to obtain risk index data, and performing standardization and spatialization processing to establish the risk index space database;
step 3, calculating the weight of the risk indexes, namely establishing a corresponding triangular fuzzy number judgment matrix based on a triangular fuzzy conversion table for each risk component, calculating the triangular fuzzy number judgment matrix by using a fuzzy analytic hierarchy process to obtain the weight vector of each risk index in the risk components, and normalizing the weight vector, wherein the weight sum of the risk indexes in each risk component is 1;
step 4, generating a weighted spatial distribution diagram of risk components, namely, for each risk component, carrying out weighted summation on risk component indexes of the risk components to obtain corresponding indexes, namely, a risk index, a vulnerability and exposure index and a release capacity index;
step 5, estimating the risk of the sea traffic, namely calculating the risk index of the sea traffic according to the risk component indexes of the three risk components, so as to generate a space distribution diagram of the risk of the sea traffic, and estimating the risk of the sea traffic, wherein the calculation formula of the risk index of the sea traffic is as follows:
where Risk is the marine traffic Risk index, H is the Risk index, VE vulnerability and exposure index, and M is the remission ability index.
The invention has the following effective benefits:
(1) The invention establishes a complete set of offshore traffic risk assessment system from three aspects of dangers, fragility, exposition and alleviation capability;
(2) The invention skillfully combines the geospatial technology, the multi-criterion decision and the risk index to provide an effective method for evaluating the risk of the sea traffic;
(3) Successful implementation of the invention in the south China sea area shows that the sea traffic risk index based on the space fuzzy multi-criterion decision can be applied in a large scale. The invention provides a scientific offshore traffic risk assessment method, which overcomes the defects of the traditional landscape assessment tool.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a general flow chart of an embodiment of the present invention.
FIG. 2 is a graph of index weights of an example embodiment of the invention.
FIG. 3 is a graph showing a dangerous space distribution diagram of an embodiment of the present invention.
Fig. 4 is a spatial distribution diagram of the weakness and exposure of an example of the invention.
FIG. 5 is a spatial distribution diagram of the mitigation capabilities of an example of the present invention.
FIG. 6 is a diagram illustrating an example marine traffic risk spatial distribution.
FIG. 7 is a graph showing the results of the example of the present invention.
Detailed Description
The technical route and operation steps of the present invention will be more apparent from the following detailed description of the present invention with reference to the accompanying drawings.
The present example selects the sea area of the south sea, which is a large edge sea with an area of 3.5X10 6 km 2 The average water depth exceeds 2000m (Lan, 2013; zhou, 2019). The region is one of the most frequent regions of world maritime traffic bordering china, philippines, vietnam, welfare, singapore and malaysia. Because of factors such as water depth, rock, typhoons, pirates, etc., it is also considered a dangerous area (Knapp, 2011; wang, 2014). In 2006 to 2015, the loss due to a marine accident in the south sea area was greatest (Weng et al, 2018).
The embodiment takes the test area as an example to describe an offshore traffic risk assessment method, as shown in a flowchart of fig. 1, specifically comprising the following steps:
In the step, through comprehensive document review, risk influence factors of the marine traffic are cleared, and the risk factors are further summarized into three components: and finally determining an offshore traffic risk assessment index system.
The risk component contains 8 risk indicators:
depth of water: sea area water depth value; typhoon average wind pressure: the average wind pressure of all typhoons in a specified time threshold; typhoon frequency: specifying the times of typhoons within a time threshold; high wind frequency: the occurrence times of strong wind in a specified time threshold; high wave frequency: designating the occurrence times of the rough waves within a time threshold; daily average sea fog coverage: daily average sea fog coverage within a specified time threshold; daily average rainfall: daily average rainfall within a specified time threshold; pirate frequency: the number of pirate events occurring within a specified time threshold. In this example, the specified time threshold is 20 years, and 10 years or 15 years may be selected.
The fragile and exposed components contained 4 risk indicators:
the shortest Euclidean distance from the coastline; shortest port distance: shortest Euclidean distance from port; shortest island distance: shortest Euclidean distance from island; ship density: number of vessels per unit area per unit time per unit sea area.
The remission ability component comprises 2 risk indicators:
the search and rescue reach time: the shortest time to reach the accident location from the search and rescue base; shortest route distance: shortest euclidean distance from the offshore route.
Step 2, establishing an index space database, namely downloading all original data related to the risk index, including space data and non-space data, processing the original data to obtain the risk index data, and carrying out standardization and spatialization processing to establish the risk index space database.
In the step, all the original data related to the risk index are downloaded, the original data are calculated according to an index system to obtain the risk index value, then all the index data are standardized by using a Min-Max method, and are spatially processed by Arcgis10.3 software to establish a risk index spatial database.
The risk index is calculated as follows:
1) The dangerous component index calculation formula is as follows:
(1) depth of water:
wherein WD k B is the water depth index value of grid k k Is the water depth raw data value of grid k. A water depth greater than 20 meters is considered a safe cruising water depth (Wang, 2014).
(2) Average wind pressure of typhoon
Wherein TP k Is the typhoon wind pressure index value at grid k, tp ik Wind pressure at grid k for the ith typhoon, n is the total number of typhoons occurring.
(3) Typhoon frequency
Wherein TF is k For the typhoon frequency index value at grid k, f ki For the number of times the ith typhoon passes through grid k, n is the total number of typhoons occurring.
(4) High wind frequency
Wherein GF (glass fiber) k For the index value of the strong wind frequency, z kj The number of times the wind passes through grid k on day j, m being the total number of days within the time threshold.
(5) High wave frequency
Wherein GF (glass fiber) k Is the index value of the wave frequency, w kj The number of times the j-th day wave passes through grid k is given, and m is the total number of days within the time threshold.
(6) Daily average sea fog coverage
Wherein F is k Is a daily average sea fog coverage index value, V jk The percentage of the day j that the mist covered grid k, m, is the total number of days within the time threshold.
(7) Daily average rainfall
Wherein P is k Is the daily average rainfall index value, P jk For the amount of rainfall at grid k on day k, m is the total number of days within the time threshold.
(8) Pirate frequency
Wherein PF is k For the pirate frequency index value, y kl For the number of times a pirate event l occurs at grid k, h is the total number of pirate events within the time threshold.
2) The fragile and exposed component index is calculated as follows:
(1) the three indexes of the shortest coast distance, (2) the shortest harbor distance and (3) the shortest island distance are all Euclidean distances:
wherein D (p q ,p v ) For point p q And point p v Euclidean distance between two points, point p q Is (x) q ,y q ) Dots (dot)p v Is (x) v ,y v )。
(4) Ship density
Wherein S is k A ship density index value e for grid k kc The number of times that the ship c appears at the grid k is the unit sea area, and g is the total number of ships within the time threshold.
3) The remission capability component index is calculated as follows:
(1) time of arrival for search and rescue
Wherein T is bk For searching and rescuing the reachable time index value L k For the journey through grid k, S k For the ship speed through grid k, r is the total number of grids that need to be traversed from the search and rescue base to the location of the accident.
(2) Shortest route distance
The Euclidean distance calculation is adopted:
wherein D (p q ,p v ) For point p q And point p v Euclidean distance between two points, point p q Is (x) q ,y q ) Point p v Is (x) v ,y v )。
And step 3, calculating the weight of the risk index, namely scoring the evaluation index by using a fuzzy analytic hierarchy process, so as to determine the relative importance of the evaluation index and obtain the corresponding weight of each index.
The specific method in the step is as follows: (1) the triangle fuzzy number judgment matrix is established based on a triangle fuzzy conversion scale (see paper Ho, C.C.,2011.Optimal evaluation of infectious medical waste disposal companies using the fuzzy analytic hierarchy process.Waste Manage.31 (7), 1553-1559), and specifically, the triangle fuzzy comparison matrix is established according to the importance degree of each risk index in the risk component to the offshore traffic risk assessment. The importance of the risk indicator may be determined by inviting an expert or based on experience or common sense. The triangular fuzzy comparison matrix can be directly adopted if the prior researchers do. (2) And (3) calculating the triangular fuzzy number judgment matrix by using a fuzzy analytic hierarchy process (see Chang, D.Y.,1996.Applications of the extent analysis method on fuzzy AHP.Eur.J.Oper.Res.95 (3), 649-655.) to obtain a weight vector of each risk index in the risk component. (3) The weight vector is normalized, and the sum of the weights of the risk indexes in each risk component is 1. The results are shown in FIG. 2. Among the dangerous components, typhoons are the most important influencing factor; ship density then affects significantly the vulnerability and exposure; in the alleviation capability, the search and rescue capability level is a main influencing factor for reducing the risk of maritime traffic.
Step 4, calculating risk indexes of the risk components, namely, for each risk component, carrying out weighted summation on the risk component indexes to obtain corresponding risk component indexes, wherein the calculation formula is as follows:
wherein Z is k Is the index value of component k, w ki Is the index i weight of component k, so the index weight sum is1, x ki Is an index value of index i of component k.
A component spatial distribution weighted map, i.e., a risk spatial distribution map, a vulnerability and exposure spatial distribution map, a relief capacity spatial distribution map, may be generated based on the risk index of the risk component. Fig. 3 shows the spatial distribution of risk in the investigation region, with darker colors representing higher risk, which in this case is classified into five classes according to the index size. The results indicate that about 13.1% and 31.5% of the south sea is located in high and very high risk areas. These areas are located mainly in the middle and north of the south sea. The dangerous area in the middle accounts for 14.3% of the area of the south sea, and is mainly concentrated in the middle and north coastal areas of the south sea. The low and very low risk areas account for 20.8% and 20.3% of the total area of south sea, respectively, with the majority located in the south of south sea. Several important factors such as high wind frequency, high wave frequency, high typhoon frequency, low visibility, etc. are the main reasons for the high and very high risk areas in the disaster graph. In contrast, the south area of south sea is less dangerous. Fig. 4 shows spatial distribution of the weakness and exposure of the study area, with darker color representing higher weakness and exposure, which in this example are classified into five classes according to the index size. Medium, high, very Gao Cuiruo and exposed areas account for 55.4%, which are mostly located in coastal areas of the south sea. The high density of vessels near coastlines and ports is a major factor affecting high vulnerability and high exposure profiles. In contrast, very low or very low levels of weakness and exposure cover 44.6% of the south sea, focusing on the middle sea area away from the coastline. The main reason for these differences is that these areas are far from coastlines and ports and the ship traffic is relatively low. Generally, lower offshore activity levels reduce the sensitivity of these areas to dangerous adverse effects. Fig. 5 is a spatial distribution diagram of the relief capacity of the study area, with lighter colors representing higher relief capacity, which in this example is divided into five classes according to the index size. The results show that 19.2% of the south sea area has very high alleviation capability. While regions classified as high level of palliative power account for 29.8% of the area of south sea. Most areas, which are classified as high or very high relief capacity, are located along coastlines near the infrastructure of maritime search and rescue sites and the like. However, most of the sea areas far from the coastline are divided into medium, low or very low levels of relief capacity (23.9%, 19.5% and 7.6% of the south sea, respectively). These areas, particularly remote areas, lack adequate relief measures.
Step 5, estimating the risk of the sea traffic, namely calculating the risk index of the sea traffic according to the risk component indexes of the three risk components, so as to generate a space distribution diagram of the risk of the sea traffic, and estimating the risk of the sea traffic, wherein the calculation formula of the risk index of the sea traffic is as follows:
where Risk is the marine traffic Risk index, H is the Risk index, VE vulnerability and exposure index, and M is the remission ability index.
Classifying the marine traffic risk indexes into 5 grades according to the numerical value and the size: very high, medium, low, very low, thereby generating a graded marine traffic risk spatial profile. And the sea traffic risk is estimated according to the classified sea traffic risk space distribution diagram, so that the sea traffic risk is more visual.
The sea traffic risk spatial variation is shown in fig. 6. Very high risk areas account for 19.8% of the south sea area, while high risk areas account for 19.1%. These areas are mainly located in north south China, the middle section of the route from singapore to hong Kong China, the Mallotus strait and the southwest coastal region of the Philippines. The risk area accounts for 20.7% of the sea area in south China sea. Most are located on offshore routes from singapore to hong Kong and igneous strait in China and in southwest coastal areas of the balanoscope island. The low risk area and very low risk area account for 40.4% of the south sea area. These areas are mainly located in the middle and south of the south sea. Important factors contributing to high risk, extremely high risk include the proximity of coastlines and ports, high ship density, strong influence of meteorological conditions and lower mitigation capacity. The south and middle parts of south sea are at relatively low risk, mainly because of the low risk level and good alleviation capability in the regions. The result is verified by adopting historical accident data, as shown in fig. 7, the comprehensive percentage of the accidents in the high risk area and the very high risk area is 81.5 percent, which is higher than 80 percent, and the offshore traffic risk assessment method provided by the invention has higher reliability.
The method for evaluating the risk of the maritime traffic is not limited to the specific technical scheme in the embodiment, and the technical scheme formed by adopting equivalent substitution is the protection scope of the invention.
Claims (5)
1. An offshore traffic risk assessment method comprising the steps of:
step 1, establishing an offshore traffic risk assessment index system, wherein the offshore traffic risk is composed of three risk components: 1) risk, 2) frailty and exposure, 3) ability to alleviate, forming an offshore traffic risk assessment index system; wherein,,
the risk component contains 8 risk indicators: water depth, typhoon average wind pressure, typhoon frequency, strong wind frequency, strong wave frequency, daily average sea fog coverage, daily average rainfall and pirate frequency;
the fragile and exposed components contained 4 risk indicators: shortest coast distance, shortest port distance, shortest island reef distance, and ship density;
the remission ability component comprises 2 risk indicators: the search and rescue time and the shortest route distance;
step 2, establishing a risk index space database, namely downloading all original data related to the risk index, processing the original data to obtain risk index data, and performing standardization and spatialization processing to establish the risk index space database;
step 3, calculating the weight of the risk indexes, namely establishing a corresponding triangular fuzzy number judgment matrix based on a triangular fuzzy conversion table for each risk component, calculating the triangular fuzzy number judgment matrix by using a fuzzy analytic hierarchy process to obtain the weight vector of each risk index in the risk components, and normalizing the weight vector, wherein the weight sum of the risk indexes in each risk component is 1;
step 4, calculating corresponding indexes of risk components, namely, weighting and summing the indexes of the risk components aiming at each risk component to obtain corresponding indexes, namely, a risk index, a frailty and exposure index and a remission capability index;
step 5, estimating the risk of the sea traffic, namely calculating the risk index of the sea traffic according to the risk component indexes of the three risk components, so as to generate a space distribution diagram of the risk of the sea traffic, and estimating the risk of the sea traffic, wherein the calculation formula of the risk index of the sea traffic is as follows:
wherein Risk is an offshore traffic Risk index, H is a Risk index, VE is a frailty and exposure index, and M is a remission ability index;
in step 1, each risk indicator in the risk component is defined as follows:
depth of water: sea area water depth value; typhoon average wind pressure: the average wind pressure of all typhoons in a specified time threshold; typhoon frequency: specifying the times of typhoons within a time threshold; high wind frequency: the occurrence times of strong wind in a specified time threshold; high wave frequency: designating the occurrence times of the rough waves within a time threshold; daily average sea fog coverage: daily average sea fog coverage within a specified time threshold; daily average rainfall: daily average rainfall within a specified time threshold; pirate frequency: the occurrence times of pirate events in a specified time threshold;
in step 1, the risk indicators in the fragile and exposed components are defined as follows:
shortest coast distance: the shortest Euclidean distance from the coastline; shortest port distance: shortest Euclidean distance from port; shortest island distance: shortest Euclidean distance from island; ship density: the number of ships in the unit sea area per unit time;
in step 1, each risk indicator in the capacity-to-alleviate component is defined as follows:
the search and rescue reach time: the shortest time to reach the accident location from the search and rescue base; shortest route distance: the shortest Euclidean distance from the offshore route;
in the step 2, the Min-Max method is used for standardizing the risk index data and carrying out standardized treatment;
in the step 3, a triangular fuzzy comparison matrix is established according to the importance degree of each risk index in the risk component to the risk assessment of the marine traffic.
2. The method for risk assessment of marine traffic according to claim 1, wherein: the specified time threshold is10 years, 15 years, or 20 years.
3. The method for risk assessment of marine traffic according to claim 1, wherein: in step 4, a weighted spatial distribution map of each risk component, namely a dangerous spatial distribution map, a fragile and exposed spatial distribution map and a remission capability spatial distribution map, is generated according to the risk index of each risk component.
4. The method for risk assessment of marine traffic according to claim 1, wherein: classifying the marine traffic risk indexes according to the numerical values, thereby generating a classified marine traffic risk spatial distribution map.
5. The method for risk assessment of marine traffic according to claim 4, wherein: in step 5, the maritime traffic risk index is divided into 5 grades according to the big-small scale: very high, medium, low, very low.
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