CN111260213B - Water resource risk assessment method for multiple risk sources of cascade reservoir group - Google Patents
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Abstract
The invention discloses a water resource risk assessment method for multiple risk sources of a cascade reservoir group, which comprises the following steps: identifying structural characteristics of a cascade reservoir group system of a water diversion project; identifying risk sources corresponding to each unit reservoir of the water diversion project respectively; constructing a nested Bayesian network risk assessment model of the cascade reservoir group of the water diversion project according to the structural characteristics and the risk sources, and obtaining the risk level of the water resource of the water diversion project through model analysis; performing sensitivity analysis through a nested Bayesian network risk assessment model, finding out key factors of risks, and formulating corrective measures; and (3) utilizing model analysis to further analyze the overall risk level of the step reservoir group of the water diversion project after the improvement, stopping the analysis if the overall risk level reaches an acceptable risk level, and searching new risk key factors if the overall risk level does not reach an expected risk level until the overall risk level is acceptable.
Description
Technical Field
The invention relates to a water resource risk assessment method, in particular to a water resource risk assessment method for multiple risk sources of a cascade reservoir group series-parallel structure, and belongs to the field of water resource risk assessment.
Background
The water diversion project aims to reasonably allocate water resources, and the water resources are diverted from the water-rich area into the water-deficient area. The water-regulating engineering has the characteristics of large engineering scale, long water-regulating distance, many hydraulic buildings, complex environment, numerous pollution sources along the way and the like. After the normal operation of the engineering, people rely more and more on the engineering, and once the water supply function is lost or the water supply quality is polluted, the life production of people can be seriously influenced, and the loss and the consequences after the engineering loss are also intolerable. Due to the complexity of hydrologic factors and the limitation of human awareness, the safe operation of water diversion has strong uncertainty, and the normal development of engineering benefits is restricted.
At present, the risk of water resources of the water diversion project is insufficient in consideration of the risk transfer and shared characteristics among units of the water diversion project structural group, and most of the risks are analyzed from a single risk source in a project whole, so that the research on the combined effect of multiple risk sources in actual projects is also lacking.
Therefore, a new judging method is needed to be designed to identify risk key factors in multiple risk sources of the cascade reservoir group water resources of the water diversion project, and project adjustment strategies are formulated from key influence factors so as to reduce or reduce the probability of overall risk occurrence.
Disclosure of Invention
The invention aims to provide a water resource risk assessment method for multiple risk sources of a cascade reservoir group, which reduces or reduces the probability of risk occurrence and improves the safety and stability of the whole engineering.
The purpose of the invention is realized in the following way: a water resource risk assessment method for multiple risk sources of a cascade reservoir group series-parallel structure comprises the following steps:
step 1) identifying structural characteristics of a cascade reservoir group system of a water diversion project;
step 2) identifying risk sources of reservoir units of the water diversion project;
step 3) constructing a nested Bayesian network risk assessment model according to the structural features and the risk sources, and obtaining the risk level of the water resource of the water diversion project through model analysis
Step 4) performing sensitivity analysis through a nested Bayesian network risk assessment model, finding out key factors of risks, and formulating corrective measures;
step 5) utilizing model analysis to obtain the overall risk level of the cascade reservoir group of the post-remediation water diversion project, stopping analysis if the overall risk level reaches an acceptable risk level, returning to the step 4) if the overall risk level does not reach an expected risk level, searching new risk key factors for further analysis until the overall risk level reaches the acceptable risk level
As a further definition of the invention, the structural features in step 1) include: serial structure, parallel structure and serial-parallel hybrid structure.
As a further definition of the invention, the risk sources in step 2) include engineering risk, hydrologic risk, economic risk, environmental risk, ecological risk, and the like.
As a further definition of the invention, step 3) specifically comprises: in the structure learning, comprehensively considering the model structure building methods such as dependency analysis, scoring searching, expert opinion and the like according to the serial-parallel characteristics of the cascade reservoir group of the water diversion project; in parameter learning, different methods are adopted for different risk sources, such as hydrologic risks mainly refer to flood risks, and the determination of the probability of the risk nodes depends on a hydrologic statistical method, a reservoir flood control algorithm or a Monte Carlo random simulation method.
As a further definition of the present invention, the specific method for finding out the key factor of risk by sensitivity analysis in step 4) is: obtaining the change of the probability table of the target node variable by changing the probability table of the selected node variable; the importance of the selected node variable B to the target node variable A is represented by an importance index I:
wherein P (A) is the prior probability of the target node variable A, and P (A|B) is the conditional probability of the target node A under the change of the selected node variable B; the importance index I reflects the influence degree of the selected node variable on the target node, and important factors influencing the safe operation of the water resource of the water transfer project are obtained by comparing the sizes of the I.
Compared with the prior art, the invention has the beneficial effects that: the invention is based on a cascade reservoir group structure of a water diversion project; by adopting a large system decomposition-coordination theory and a process control technology, a whole set of model nesting construction methods of a single bank-single risk source, a single bank-multiple risk source, a serial bank-single risk source, a serial bank-multiple risk source and a serial-parallel bank-multiple risk source are provided by points and planes, and a water resource risk assessment model of the multiple risk sources of the cascade reservoir group of the water transfer project is established.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a bayesian network structure diagram of the unit reservoir water resource risk under the effect of exceeding flood.
Fig. 3 is a bayesian network structure of unit reservoir water resource risk under the combined action of exceeding flood and water pollution events.
Fig. 4 is a bayesian network structure of the risk of combining a continuous reservoir water resource with a superscalar flood and a sudden water pollution event.
Fig. 5 is a schematic diagram of a simple bayesian network structure for water resource multi-risk assessment of a cascade reservoir group series-parallel structure.
Detailed Description
The water resource risk assessment method for the multi-risk source of the cascade reservoir group as shown in fig. 1 comprises the following steps:
s101, identifying structural characteristics of a step reservoir group system of a water diversion project, wherein the structural characteristics comprise: a series structure, a parallel structure and a series-parallel hybrid structure;
s102, identifying risk sources of reservoirs of each unit of the water diversion project, wherein the risk sources comprise project risk, hydrologic risk, economic risk, environmental risk, ecological risk and the like;
s103, constructing a nested Bayesian network risk assessment model of 'single bank-single risk source', 'single bank-multiple risk source', 'serial bank-single risk source', 'serial bank-multiple risk source', 'serial-parallel bank-multiple risk source' according to the structural characteristics and the risk sources, and obtaining the water resource risk level of the water diversion project; the method specifically comprises the following steps: in the structure learning, comprehensively considering the model structure building methods such as dependency analysis, scoring searching, expert opinion and the like according to the serial-parallel characteristics of the cascade reservoir group of the water diversion project; in parameter learning, different methods are adopted aiming at different risk sources, for example, hydrologic risks mainly refer to flood risks, and the node probability is determined by means of a hydrologic statistical method, a reservoir flood control algorithm or a Monte Carlo random simulation method;
s104, performing sensitivity analysis through a nested Bayesian network risk assessment model, finding out key factors of risks, and formulating corrective measures; the specific method comprises the following steps: obtaining the change of the probability table of the target node variable by changing the probability table of the selected node variable; the importance of the selected node variable B to the target node variable A is represented by an importance index I:
wherein P (A) is the prior probability of the target node variable A, and P (A|B) is the conditional probability of the target node A under the change of the selected node variable B; the importance index I reflects the influence degree of the selected node variable on the target node, and important factors influencing the safe operation of the water resource of the water transfer project are obtained by comparing the sizes of the I;
s105, utilizing model analysis to obtain the overall risk level of the cascade reservoir group of the water diversion project after the renovation, stopping analysis if the overall risk level reaches an acceptable risk level, returning to the step 4) if the overall risk level does not reach an expected risk level, and searching new risk key factors for further analysis until the overall risk level reaches the acceptable risk level.
The invention will be further illustrated with reference to specific examples.
And constructing a nested Bayesian network risk assessment model of 'single library-single risk source', 'single library-multiple risk source', 'serial library-single risk source', 'serial library-multiple risk source', 'serial-parallel library-multiple risk source' by points and planes.
First, as shown in FIGS. 2-4, in the "Single library-Single risk Source" of FIG. 2, P (F 0 ) The prior probability of the occurrence of the exceeding flood is mainly adopted according to the hydrologic data statistics seriesCalculating by a statistical method or a Copula function method; p (O) is the probability of reservoir failure; p (O|F) 0 ) Conditional probability for flood flooding (dam collapse);
P(O)＝P(F 0 )·P(O|F 0 )。
in the single-reservoir-multiple risk source of FIG. 3, P (R) represents the probability of oil spill pollution of the reservoir, P (T) represents the probability of failure caused by sudden water pollution of the reservoir under the action of the oil spill pollution, and P (T|R) represents the conditional probability of failure of the reservoir caused by the oil spill pollution; p (Q) represents the probability of reservoir bridge vehicle chemical contamination, and P (T|Q) represents the conditional probability of reservoir failure when bridge vehicle chemical contamination occurs. P (W) represents the probability of abnormal sewage discharge of the reservoir, and P (T|W) represents the conditional probability of reservoir failure caused by abnormal sewage discharge.
According to Bayesian theory and related characteristics, the combination condition of occurrence (Yes) and non-occurrence (No) of each risk source factor is considered, and the probability calculation method for causing single reservoir failure under the combined action of the exceeding flood and sudden water pollution can be obtained, wherein the probability calculation method comprises the following steps:
P(O A ＝Y)＝∑ i＝Y，N (P(F O ＝i)P(O A ＝Y|F O ＝i)+P(T＝i)P(O A ＝Y|T＝i))
wherein the method comprises the steps of
P(T＝Y)＝∑ i＝Y，N (P(R＝i)P(T＝Y|R＝i)+P(Q＝i)P(T＝Y|Q＝i)+P(W＝i)P(T＝Y|W＝i))。
In the figure 4, the reservoirs are connected in series and are provided with multiple risk sources, for two adjacent upstream and downstream reservoirs, each reservoir needs to consider the risk of reservoir failure under the combined effect of exceeding flood and sudden water pollution event, and the downstream reservoir needs to consider the risk of reservoir failure after the upstream reservoir. According to the condition independence principle and the probabilistic reasoning formula of the Bayesian network, the combination condition of occurrence (Yes) and non-occurrence (No) of each risk source factor is considered, and the probability calculation method for causing continuous two adjacent reservoirs to lose efficacy under the combined action of the exceeding flood and the sudden water pollution can be obtained, wherein the probability calculation method comprises the following steps:
P(O B ＝Y)＝∑ i＝Y，N (P(F A ＝i)P(O B ＝Y|F A ＝i)+P(F B ＝i)P(O B ＝Y|F B ＝i)+P(T＝i)P(O B ＝Y|T＝i))
wherein:
P(T＝Y)＝∑ i＝Y，N (P(R＝i)P(T＝Y|R＝i)+P(Q＝i)P(T＝Y|Q＝i)+P(W＝i)P(T＝Y|W＝i))
fig. 5 is a schematic diagram of a simple structure of a bayesian network water resource risk analysis and evaluation model.
The invention is not limited to the above embodiments, and based on the technical solution disclosed in the invention, a person skilled in the art may make some substitutions and modifications to some technical features thereof without creative effort according to the technical content disclosed, and all the substitutions and modifications are within the protection scope of the invention.
Claims (4)
1. The water resource risk assessment method for the multi-risk source of the cascade reservoir group is characterized by comprising the following steps of:
step 1) identifying structural characteristics of a cascade reservoir group system of a water diversion project, wherein the structural characteristics comprise: a series structure, a parallel structure and a series-parallel hybrid structure;
step 2) identifying risk sources corresponding to each unit reservoir of the water diversion project respectively;
step 3) constructing a nested Bayesian network risk assessment model of the cascade reservoir group of the water diversion project according to the structural characteristics and the risk sources, and obtaining the risk level of the water resource of the water diversion project through model analysis; the nested bayesian network risk assessment model comprises: single bank-single risk source, single bank-multiple risk source, serial bank-single risk source, serial bank-multiple risk source, serial parallel bank-multiple risk source;
step 4) performing sensitivity analysis through a nested Bayesian network risk assessment model, finding out key factors of risks, and formulating corrective measures;
and 5) utilizing model analysis to obtain the overall risk level of the cascade reservoir group of the water diversion project after the renovation, stopping the analysis if the overall risk level reaches an acceptable risk level, returning to the step 4) if the overall risk level does not reach an expected risk level, and searching new risk key factors for further analysis until the overall risk level reaches the acceptable risk level.
2. The method of assessing the risk of water resources of a multi-risk source for a cascade reservoir group according to claim 1, wherein the risk sources in step 2) include engineering risk, hydrologic risk, economic risk, environmental risk and ecological risk.
3. The method for evaluating the risk of water resources of multiple risk sources for a cascade reservoir group according to claim 2, wherein the building of the nested bayesian network risk evaluation model in step 3) specifically comprises: in the structure learning, comprehensively considering a dependence analysis, a grading search and an expert opinion model structure building method according to the serial-parallel characteristics of the cascade reservoir group of the water diversion project; in parameter learning, different methods are adopted for different risk sources, wherein the hydrologic risk mainly comprises flood risk, and the determination of the probability of risk nodes depends on a hydrologic statistical method, a reservoir flood control algorithm or a Monte Carlo random simulation method.
4. The method for evaluating the risk of water resources of multiple risk sources for a cascade reservoir group according to claim 3, wherein the specific method for finding out the key factors of the risk by sensitivity analysis in the step 4) is as follows: obtaining the change of the probability table of the target node variable by changing the probability table of the selected node variable; the importance of the selected node variable B to the target node variable A is represented by an importance index I:
wherein P (A) is the prior probability of the target node variable A, and P (A-B) is the conditional probability of the target node A under the change of the selected node variable B; the importance index I reflects the influence degree of the selected node variable on the target node, and important factors influencing the safe operation of the water resource of the water transfer project are obtained by comparing the sizes of the I. />
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