CN111401702A - Maritime traffic risk assessment method - Google Patents
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Abstract
The invention relates to a maritime traffic risk assessment method, which comprises the following steps: firstly, constructing a maritime traffic risk assessment index system, wherein the index system comprises three risk components: 1) and risk; 2) vulnerability and exposure; 3) the ability to alleviate; secondly, establishing an evaluation index space database; thirdly, calculating the evaluation index weight, namely calculating the index weight of each risk component; fourthly, generating a component weighted graph; fifthly, marine traffic risk assessment, namely calculating a marine traffic risk index, and further classifying the marine traffic risk index into 5 grades: very high, medium, low, very low. The method analyzes the internal driving factors of the marine traffic risk, increases the transparency of the marine traffic risk, and provides important technical support for reducing the possibility of marine accidents. The combination of the geospatial technology, the multi-criterion decision-making and the risk index provides a scientific marine traffic risk assessment method, and the defects of marine traffic risk tools are overcome.
Description
Technical Field
The invention relates to a marine traffic risk assessment method, in particular to a marine traffic risk assessment method based on space multi-attribute decision.
Background
Economic globalization has facilitated the rapid development of maritime commerce. Due to the low cost nature of maritime, about 90% of the world's trade is through maritime transport (Baksh, 2018). However, with the rapid growth in the size and number of ships, the frequency of marine accidents is also increasing (Knap, 2017;
2017). The consequences of an accident may cause serious life and property damage and damage to the marine environment (Heij, 2011; Zhou, 2019). Thus, ship voyage safety is a continuing concern of maritime authorities, the shipping industry and society, and emphasizes the importance of maritime traffic risk assessment (Huang, 2019).
With respect to marine transportation risks, a number of qualitative and quantitative assessments have been made. Generally, maritime transport risk assessment studies are based on maritime reports and statistical methods (Zhang, 2019). For example, Zhang et al evaluated the shipping risk of a changjiang waterway based on a bayesian network. Vander et al propose a new multi-level approach to assessing and predicting marine transport risk. 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 grounding risk using the Monte Carlo method. These research methods can be used to assess the long-term risk status of a ship. However, the maritime based reporting and statistical methods cannot spatially present risk information. To address this problem, Wang et al employ a fuzzy analytic hierarchy process to analyze spatial variations in airway risk. Zhang provides a shipping risk assessment method based on a grey correlation theory, and generates a shipping risk map. While marine traffic risk assessment has made some progress on a spatial scale, most methods have limited criteria used in the evaluation process. In particular, there is currently no comprehensive analytical method to determine the overall voyage risk by taking into account the driving factors (hazards, vulnerabilities, exposure and mitigation abilities) of marine traffic risks. On a spatial scale, a good understanding of risk drivers is crucial for the formulation of risk mitigation measures and policies (rotlisberger, 2017). Spatial analysis can effectively mine the distribution characteristics of risks in space (Hoque, 2019). Although such methods are used in many applications, they have not found effective use in marine risk assessment (Vander, 2015). Obviously, a spatial approach must be developed to assess the risk of a ship navigating in a marine environment.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the defects in the prior art are overcome, and the marine traffic risk assessment method is provided. Compared with the traditional method, the invention provides a scientific assessment method for the maritime traffic risk assessment by combining the geospatial technology, the multi-criterion decision and the risk index.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: the marine traffic risk assessment method comprises the following steps:
step 1, establishing a maritime traffic risk assessment index system, wherein the maritime traffic risk consists of three risk components: 1) danger, 2) fragility and exposition, 3) alleviation capacity, and a maritime traffic risk assessment index system is formed; wherein the content of the first and second substances,
the risk component contains 8 risk indicators: water depth, typhoon average wind pressure, typhoon frequency, gale frequency, daily average sea fog coverage, daily average rainfall, pirate frequency;
the fragile and exposition components contain 4 risk indicators: the shortest coast distance, the shortest port distance, the shortest island distance and the ship density;
the remission capacity component contains 2 risk indicators: the search and rescue reachable time and the shortest route distance;
step 2, establishing a risk index spatial database, namely downloading all original data related to risk indexes, processing the original data to obtain risk index data, carrying out standardization and spatialization processing, and establishing the risk index spatial database;
step 3, risk index weight calculation, 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 a weight vector of each risk index in the risk components, and standardizing the weight vector, wherein the weight sum of the risk indexes in each risk component is 1;
step 4, generating a risk component weighted spatial distribution diagram, namely carrying out weighted summation on risk component indexes of each risk component to obtain corresponding indexes, namely a risk index, a vulnerability and exposure index and a remission capacity index;
step 5, marine traffic risk assessment, namely calculating a marine traffic risk index according to risk component indexes of the three risk components, thereby generating a marine traffic risk spatial distribution map and assessing marine traffic risks, wherein the marine traffic risk index has the following calculation formula:
wherein Risk is a maritime traffic Risk index, H is a Risk index, VE vulnerability and exposition index, and M is a remission capacity index.
The effective benefits of the invention are as follows:
(1) the invention establishes a set of complete maritime traffic risk assessment system in three aspects of danger, fragility, exposition and relieving capacity;
(2) the invention ingeniously combines the geospatial technology, the multi-criterion decision and the risk index to provide an effective method for evaluating the marine traffic risk;
(3) successful implementation of the method in the south China sea area shows that the marine traffic risk index based on the space fuzzy multi-criterion decision can be applied in a large scale. The invention provides a scientific maritime traffic risk assessment method, which overcomes the defects of the traditional landscape evaluation tool.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a general flow diagram of an embodiment of the present invention.
FIG. 2 is a schematic overview of an exemplary study area of the present invention.
FIG. 3 is a graph of index weights for an embodiment of the present invention.
FIG. 4 is a graph of risk space distribution for an example of the present invention.
FIG. 5 is a spatial distribution diagram of vulnerability and exposure of an example of the present invention.
FIG. 6 is a spatial distribution plot of mitigation capabilities for an example of the present invention.
FIG. 7 is a marine traffic risk spatial distribution plot of an example of the present invention.
FIG. 8 is a graph of verification of the results of an example of the present invention.
Detailed Description
The technical route and the operation steps of the present invention will be more clearly understood 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 China sea, which is a large marginal sea with an area of 3.5 × 106km2The average water depth exceeds 2000m (L an, 2013; Zhou, 2019)One of The frequent regions borders china, philippines, vietnam, welan, singapore and malaysia, and The main routes in this sea area are shown in fig. 2 (The world architecture, 2015). It is also considered a dangerous area because of water depth, rocks, typhoons, pirates, etc. (Knapp, 2011; Wang, 2014). The losses due to ship accidents occurring in the sea area of south sea were greatest from 2006 to 2015 (Weng et al, 2018).
The embodiment describes a method for evaluating a marine traffic risk by taking the test area as an example, as shown in a flowchart of fig. 1, the method specifically includes the following steps:
step 1, establishing a marine traffic risk assessment index system, namely clearing marine traffic risk influence factors; marine traffic risk is divided into three risk components: 1) and risk; 2) vulnerability and exposure; 3) and relieving capacity. And establishing corresponding indexes of the risk components to form a maritime traffic risk assessment index system.
In the step, through comprehensive literature review, the influence factors of the marine traffic risk are cleared up, and the risk factors are further summarized into three components: dangerousness, fragility and exposition, relieving capacity, and finally determining a maritime traffic risk assessment index system.
The risk component contains 8 risk indicators:
water depth: sea water depth value; typhoon average wind pressure: the average wind pressure of all typhoons within a specified time threshold value; typhoon frequency: assigning the number of typhoon times within a time threshold; the big wind frequency: specifying the number of times of occurrence of strong wind within a time threshold; big wave frequency: specifying the number of large wave generation within a time threshold; daily average sea fog coverage: daily average sea fog coverage within a specified time threshold; average daily rainfall: average daily 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 may be 10 years or 15 years.
The fragile and exposition components contain 4 risk indicators:
the shortest euclidean distance from the coastline; shortest port distance: the shortest Euclidean distance from the port; shortest island distance: the shortest Euclidean distance from the reef; density of the ship: the number of ships in unit time unit sea area.
The remission capacity component contains 2 risk indicators:
search and rescue reaching time: the shortest time to reach the accident position from the search and rescue base; shortest route distance: the shortest Euclidean distance from the sea route.
And 2, establishing an index spatial database, namely downloading all original data related to the risk index, including spatial data and non-spatial data, processing the original data to obtain risk index data, and carrying out standardization and spatialization processing to establish the risk index spatial database.
In the step, firstly, all original data related to the risk indexes are downloaded, the original data are calculated according to an index system to obtain risk index values, then all the index data are standardized by using a Min-Max method, spatialization is carried out through Arcgis10.3 software, and a risk index spatial database is established.
The risk indicator is calculated as follows:
1) the dangerous component index calculation formula is as follows:
① water depth:
wherein WDkIs the water depth index value of grid k, BkIs the water depth raw data value of grid k. Water depths greater than 20 meters are considered safe sailing water depths (Wang, 2014).
② typhoon average wind pressure
Wherein, TPkIs the typhoon wind pressure index value tp at grid kikThe wind pressure of the ith typhoon at the grid k is shown, and n is the total amount of typhoons.
③ typhoon frequency
Wherein, TFkIs a grid k typhoon frequency index value, fkiThe number of times the ith typhoon passes through the grid k, and n is the total number of typhoons.
④ high wind frequency
Wherein, GFkIs a large wind frequency index value, zkjThe number of times that the strong wind passes through the grid k on the j th day is m, which is the total number of days within the time threshold.
⑤ high wave frequency
Wherein, GFkIs a value of the great wave frequency index, wkjThe number of times that the big waves pass through the grid k on the j th day is m, which is the total number of days within the time threshold.
Average daily sea fog coverage of ⑥
Wherein, FkIs a daily average sea fog coverage index value, VjkIs the percentage of day j of the sea fog covering the grid k, and m is the total number of days within the time threshold.
⑦ average rainfall per day
Wherein, PkIs a daily average rainfall index value, PjkThe rainfall at the grid k on the kth day is shown, and m is the total number of days within the time threshold.
⑧ pirate frequency
Wherein, PFkIs an index value of pirate frequency, yklThe number of times the pirate event l occurs on the grid k and h is the total number of pirate events within the time threshold.
2) The fragile and exposal component index calculation formula is as follows:
① the shortest coast distance, ② the shortest harbor distance and ③ the shortest island distance all adopt Euclidean distances:
wherein D (p)q,p5) Is a point pqAnd point pvEuclidean distance between two points, point pqHas the coordinates of (x)q,yq) Point p ofvHas the coordinates of (x)v,yv)。
④ density of ship
Wherein S iskVessel density index value for grid k, ekcThe number of times the ship c appears on the grid k, A is the unit sea area, and g is the total number of ships within the time threshold.
3) The calculation formula of the remission capacity component index is as follows:
① search and rescue time
Wherein, TbkTime index value for search and rescue, LkFor the path through the grid k, SkTo pass through the grid k, the boat speed, r, is the total number of grids that need to be passed through to reach the accident location from the search and rescue base.
② shortest route distance
And (3) calculating by adopting an Euclidean distance:
wherein D (p)q,pv) Is a point pqAnd point pvEuclidean distance between two points, point pqHas the coordinates of (x)q,yq) Point p ofvHas the coordinates of (x)v,yv)。
And 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 this step is that ① establishes a triangular fuzzy number judgment matrix based on a triangular fuzzy transformation scale (see the paper Ho, C.C.,2011.Optimal evaluation of operational spatial fuzzy complex using the fuzzy analytic process. Waste management.31 (7), 1553. 1559.), specifically, according to the importance degree of each risk index in the risk component to the marine traffic risk assessment, the establishment of the triangular fuzzy comparison matrix is performed, the importance degree of the risk index can be judged according to experience or common sense, if a triangular fuzzy comparison matrix made by a researcher exists previously, ② can be directly adopted by using fuzzy analytic hierarchy process (see Chang, D.Y.,1996.Applications of the fuzzy analytic hierarchy process) and the influence of each risk index on the risk component in the marine traffic risk vector graph is obtained by using fuzzy analytic hierarchy process analysis method (see Chang, D.Y., AHP.J.655. Optimal fuzzy analytic hierarchy process of the environmental analysis method) and the influence of each risk component in the triangular fuzzy analytic hierarchy process is shown as a triangular fuzzy analytic process vector ③, and the influence of the risk vector is reduced in the risk vector ③.
Step 4, calculating the risk indexes of the risk components, namely weighting and summing the risk component indexes of each risk component to obtain the corresponding risk component index, wherein the calculation formula is as follows:
wherein Z iskIs an index value of component k, wkiIs the weight of index i of component k, so the sum of the index weights is1, xkiIs an index value of index i of component k.
And generating component spatial distribution weighted graphs, namely a risk spatial distribution graph, a vulnerability and exposure spatial distribution graph and a remission capacity spatial distribution graph based on the risk indexes of the risk components. Fig. 4 shows a risk space distribution map of the study area, with darker colors representing higher risk, in this case the risk is divided into five levels according to the index size. The results show that about 13.1% and 31.5% of the south sea are located in high-risk and very high-risk areas. These areas are located primarily in the middle and north of the south sea. The medium risk zone accounted for 14.3% of the area in the south sea, and was primarily concentrated in the mid-south and north coastal areas. The low-risk zone and the very low-risk zone account for 20.8% and 20.3% of the total area of the south sea, respectively, the majority of which are located in the south of the south sea. Several important factors, such as high wind frequency, high wave frequency, high typhoon frequency, low visibility, etc., are the main causes of high-risk and very high-risk areas in disaster maps. In contrast, the southern regions of the south sea are less dangerous. Fig. 5 shows the spatial distribution of the vulnerability and exposure of the study area, with darker colors representing higher vulnerability and exposure, which in this example are divided into five levels according to the size of the index. The medium, high, very high fragile and exposed areas represent 55.4%, most of these areas being located in coastal areas of the south sea. The proximity to shorelines and ports and the high density of ships are major factors affecting high vulnerability and high exposure profiles. In contrast, very low or very low levels of vulnerability and exposure cover 44.6% of the south sea, concentrated in the mid-sea area far from the shoreline. The main reason for these differences is that these areas are far from the shoreline and port, and there is relatively little ship traffic. In general, lower levels of offshore activity reduce the susceptibility of these areas to adverse effects of the hazard. Fig. 6 is a spatial distribution diagram of the remission capacity of the study area, wherein lighter color represents higher remission capacity, and in this example, the remission capacity is divided into five levels according to the size of the index. The results show that 19.2% of the south sea area has a very high relief capacity. While the area classified as a high remission level accounts for 29.8% of the area in south sea. Most of the areas classified as high or very high mitigation capacity are located along the shoreline, near the infrastructure such as maritime search and rescue bases. However, most sea areas far from the coastline are classified as medium, low or very low relief capacity levels (23.9%, 19.5% and 7.6% of the south sea, respectively). These areas, particularly remote areas, lack adequate relief.
Step 5, marine traffic risk assessment, namely calculating a marine traffic risk index according to risk component indexes of the three risk components, thereby generating a marine traffic risk spatial distribution map and assessing marine traffic risks, wherein the marine traffic risk index has the following calculation formula:
wherein Risk is a maritime traffic Risk index, H is a Risk index, VE vulnerability and exposition index, and M is a remission capacity index.
The marine traffic risk index is classified into 5 grades according to the numerical value and the size: very high, medium, low, very low, thus generating a hierarchical marine traffic risk spatial profile. And the marine traffic risk is evaluated according to the graded marine traffic risk spatial distribution map, so that the method is more intuitive.
Fig. 7 shows the spatial variation of the sea traffic risk. The very high risk zone accounts for 19.8% of the sea area in the south sea, while the high risk zone accounts for 19.1%. These areas are located primarily in the north of the south sea, singapore to the midsection of hong kong routes, the malaysia strait and the southwestern coastal areas of the philippines. The stroke risk area accounts for 20.7% of the sea area of the south China sea. Most are located on marine routes from singapore to hong kong and lusong strait and in the southwestern coastal areas of balata. The low risk zone and the very low risk zone account for 40.4% of the sea area of the south sea. These areas are located primarily in the central and southern regions of the south sea. Important factors contributing to high risk, extremely high risk include the proximity of shorelines and ports, high density of ships, strong influence of meteorological conditions and low alleviation ability. The risks are relatively low in the southern and mid-regions of the south sea, mainly because these regions are less dangerous and have better relief. The results are verified by adopting historical accident data, as shown in fig. 8, the comprehensive percentage of accidents in the high risk area and the very high risk area is 81.5% and is higher than 80%, which indicates that the method for evaluating the marine traffic risk provided by the invention has higher reliability.
The method for evaluating the marine traffic risk is not limited to the specific technical scheme described in the embodiment, and all the technical schemes formed by adopting equivalent substitution are the protection scope required by the invention.
Claims (10)
1. A maritime traffic risk assessment method comprises the following steps:
step 1, establishing a maritime traffic risk assessment index system, wherein the maritime traffic risk consists of three risk components: 1) danger, 2) fragility and exposition, 3) alleviation capacity, and a maritime traffic risk assessment index system is formed; wherein the content of the first and second substances,
the risk component contains 8 risk indicators: water depth, typhoon average wind pressure, typhoon frequency, gale frequency, daily average sea fog coverage, daily average rainfall, pirate frequency;
the fragile and exposition components contain 4 risk indicators: the shortest coast distance, the shortest port distance, the shortest island distance and the ship density;
the remission capacity component contains 2 risk indicators: the search and rescue reachable time and the shortest route distance;
step 2, establishing a risk index spatial database, namely downloading all original data related to risk indexes, processing the original data to obtain risk index data, carrying out standardization and spatialization processing, and establishing the risk index spatial database;
step 3, risk index weight calculation, 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 a weight vector of each risk index in the risk components, and standardizing the weight vector, wherein the weight sum of the risk indexes in each risk component is 1;
step 4, calculating corresponding indexes of the risk components, namely weighting and summing the risk component indexes of each risk component to obtain corresponding indexes, namely a risk index, a vulnerability and exposure index and a remission capacity index;
step 5, marine traffic risk assessment, namely calculating a marine traffic risk index according to risk component indexes of the three risk components, thereby generating a marine traffic risk spatial distribution map and assessing marine traffic risks, wherein the marine traffic risk index has the following calculation formula:
wherein Risk is a maritime traffic Risk index, H is a Risk index, VE vulnerability and exposition index, and M is a remission capacity index.
2. The marine traffic risk assessment method according to claim 1, characterized in that: in step 1, the risk indicators in the risk component are defined as follows:
water depth: sea water depth value; typhoon average wind pressure: the average wind pressure of all typhoons within a specified time threshold value; typhoon frequency: assigning the number of typhoon times within a time threshold; the big wind frequency: specifying the number of times of occurrence of strong wind within a time threshold; big wave frequency: specifying the number of large wave generation within a time threshold; daily average sea fog coverage: daily average sea fog coverage within a specified time threshold; average daily rainfall: average daily rainfall within a specified time threshold; pirate frequency: the number of pirate events occurring within a specified time threshold.
3. The marine traffic risk assessment method according to claim 1, characterized in that: the specified time threshold is10 years, 15 years, or 20 years.
4. The marine traffic risk assessment method according to claim 1, characterized in that: in step 1, the risk indicators in the vulnerable and exposed components are defined as follows:
shortest coast distance: the shortest euclidean distance from the coastline; shortest port distance: the shortest Euclidean distance from the port; shortest island distance: the shortest Euclidean distance from the reef; density of the ship: the number of ships in unit time unit sea area.
5. The marine traffic risk assessment method according to claim 1, characterized in that: in step 1, each risk indicator in the remission capacity component is defined as follows:
search and rescue reaching time: the shortest time to reach the accident position from the search and rescue base; shortest route distance: the shortest Euclidean distance from the sea route.
6. The marine traffic risk assessment method according to claim 1, characterized in that: and step 2, standardizing the risk index data by using a Min-Max method and carrying out standardization processing.
7. The marine traffic risk assessment method according to claim 1, characterized in that: and 3, establishing a triangular fuzzy comparison matrix according to the importance degree of each risk index in the risk component on the marine traffic risk assessment.
8. The marine traffic risk assessment method according to claim 1, characterized in that: in step 4, a weighted spatial distribution map of each risk component is generated according to the risk index of each risk component, namely a risk spatial distribution map, a vulnerable and exposed spatial distribution map and a remission capacity spatial distribution map.
9. The marine traffic risk assessment method according to claim 1, characterized in that: and grading the marine traffic risk index according to the numerical value, thereby generating a graded marine traffic risk spatial distribution map.
10. The marine traffic risk assessment method according to claim 9, characterized in that: in step 5, the marine traffic risk index is divided into 5 grades according to the size from large to small: very high, medium, low, very low.
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