Risk evolution-based flood control benefit analysis method and system for reservoir group to cope with extreme climate

Abstract

The invention is suitable for the crossing field of hydraulic engineering and climate science, and provides a risk evolution-based flood control benefit analysis method for a reservoir group to cope with extreme climate, and the method comprises the following steps: screening extreme climate elements through multi-source data fusion, and constructing a spatial-temporal feature model; extracting extreme climatic elements, inputting the extreme climatic elements into the pre-constructed hydrological model, simulating to obtain an initial flood process, constructing a flood database, training and adjusting hydrological model parameters, and inputting the extreme climatic elements again to obtain an ultimate flood process; flood risk data are collected, a risk assessment index system and a dynamic risk simulation model are constructed, an ultimate flood process is input to calculate flood control risks, and a multi-objective optimization algorithm is adopted to generate an optimal scheduling strategy; according to the method, a reliable basis is provided for scientific scheduling and flood control decision making of the reservoir group through innovative data acquisition and analysis means, advanced model construction and a multi-dimensional evaluation system, and the flood control benefit of the reservoir group under the extreme climate condition is improved to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the intersection of water conservancy engineering and climate science, and specifically to a method and system for analyzing the benefits of flood control in response to extreme climate events based on risk evolution of a reservoir group. Background Art

[0002] With global climate change, the frequency and intensity of extreme weather events, such as rainstorms and hurricanes, have increased significantly. As important water conservancy projects, reservoirs play a key role in flood control and disaster reduction. However, traditional flood control benefit analysis methods have many limitations.

[0003] In terms of describing the characteristics of extreme climate elements, it is difficult to fully and accurately grasp the complex changes in extreme climate in time and space by relying solely on data from conventional meteorological stations. As for the relationship between extreme climate and floods, traditional hydrological models cannot fully consider the uncertainty of complex underlying surface conditions and extreme meteorological inputs. Flood risk assessment is often limited to direct economic losses and casualties, ignoring multiple impacts such as the ecological environment and social stability. The formulation of reservoir scheduling plans lacks multi-objective optimization and real-time dynamic adjustment mechanisms, making it difficult to maximize flood control benefits. Therefore, in response to the above situation, there is an urgent need to provide a risk-evolution-based flood control benefit analysis method and system for reservoir groups responding to extreme climate to overcome the shortcomings in current practical applications. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for analyzing the benefits of flood control of reservoir groups in response to extreme climate based on risk evolution, aiming to solve the problems in the above-mentioned background technology.

[0005] The present invention is achieved by a risk evolution-based method for analyzing the benefits of flood control in reservoir groups in response to extreme climates, the method comprising the following steps:

[0006] Step S1: Collect data from the study area, construct an empirical mode decomposition model and a dynamic three-dimensional meteorological spatial model, screen extreme climate elements and describe their spatiotemporal characteristics;

[0007] Step S2: extract extreme climate elements and input them into the pre-built hydrological model to simulate the initial flood process, build a flood database, train and adjust the hydrological model parameters, and then input extreme climate elements again to obtain the ultimate flood process;

[0008] Step S3: Collect flood risk data, build a risk assessment indicator system and a dynamic risk simulation model, and input the ultimate flood process to calculate flood control risks;

[0009] Step S4: Construct the optimal reservoir operation strategy set, input the ultimate flood into the multi-objective optimization operation model, calculate the flood control risk reduced by different operation strategies, and screen the optimal operation strategy.

[0010] As a further solution of the present invention: Step S1 specifically includes:

[0011] Step S11: using multi-source data fusion method to collect data of the study area and screen extreme climate elements;

[0012] Step S12: constructing an empirical mode decomposition model and a GIS-based long short-term memory network coupled dynamic three-dimensional meteorological spatial model;

[0013] Step S13: Input extreme climate elements to obtain temporal characteristics and spatial characteristics respectively.

[0014] As a further solution of the present invention: the multi-source data fusion in step S11 includes:

[0015] Step S11 a: Satellite remote sensing collects temperature and precipitation data;

[0016] Step S11 b: The ground IoT device collects wind speed, wind direction and humidity data;

[0017] Step S11 c: weather radar collects extreme weather data;

[0018] Step S11 d: Adopting an adaptive fusion algorithm to calculate the weight of each data source and filter extreme climate elements.

[0019] As a further solution of the present invention: Step S13 specifically includes:

[0020] Step S13a: Time features are obtained through empirical mode decomposition model and long short-term memory network training, including trend changes and periodic features;

[0021] Step S13b: spatial features are obtained by setting multi-layer parameters of the dynamic three-dimensional meteorological spatial model, including spatial differences and dynamic changes.

[0022] As a further solution of the present invention: Step S2 specifically includes:

[0023] Step S21: construct a distributed hydrological model based on physical processes and discretize the study area into several calculation units using the finite element method;

[0024] Step S22: extract extreme climate elements and input them into the distributed hydrological model, use Monte Carlo simulation to obtain several initial flood processes, and build a flood database;

[0025] Step S23: extract flood data from the flood database, use the first 80% as a training set and the last 20% as a test set, input the training set together with extreme climate factors and floods into the distributed hydrological model for training, and adjust the model parameters;

[0026] Step S24: using the test set to input the trained distributed hydrological model, evaluating the model based on the simulation results and adjusting the model to obtain an optimized distributed hydrological model;

[0027] Step S25: extract extreme climate elements again and input them into the optimized distributed hydrological model to simulate and obtain several ultimate flood processes.

[0028] As a further solution of the present invention: the risk assessment index system construction in step S3 includes:

[0029] Step S31: Collect flood risk data, screen flood risk indicators, and build a risk assessment indicator system;

[0030] Step S32: Construct a dynamic risk simulation model, extract the ultimate flood process as the model input, and calculate the flood control risk corresponding to each ultimate flood.

[0031] As a further solution of the present invention: Step S31 specifically includes:

[0032] Step S31a: Collect flood risk data and select ecological environment damage indicators, social stability indicators and infrastructure restoration cost indicators respectively;

[0033] Step S31 b: Use the analytic hierarchy process to construct a judgment matrix, calculate the weights of each flood risk indicator based on expert opinions, and build a risk assessment indicator system.

[0034] As a further solution of the present invention: Step S32 specifically includes:

[0035] Step S32a: construct a dynamic risk simulation model based on the hydrological model, and collect population distribution data, economic value data, and infrastructure distribution data of the study area;

[0036] Step S32b: extracting the ultimate flood process and inputting it into the dynamic risk simulation model, and using Monte Carlo simulation to obtain the flood inundation range, water depth, and duration corresponding to different floods;

[0037] Step S32c: For each simulation result, the probability distribution of losses is calculated based on the population distribution data, economic value data and infrastructure distribution data, and the flood control risk corresponding to each ultimate flood is calculated.

[0038] As a further solution of the present invention: Step S4 specifically includes:

[0039] Step S41: Construct a multi-objective optimization scheduling model, in which the flood control objective is measured by the degree of flood risk reduction, the power generation objective is based on maximizing power generation as the objective function, the water supply objective is based on meeting downstream water demand as a constraint condition, and the ecological objective is based on maintaining the ecological base flow of the river;

[0040] Step S42: input each ultimate flood into the multi-objective optimization operation model in sequence, use the non-dominated sorting genetic algorithm to solve the model, screen out the optimal reservoir operation strategy, and construct the optimal reservoir operation strategy set;

[0041] Step S43: input each ultimate flood into the multi-objective optimization operation model in turn and respectively adopt all reservoir operation strategies in the optimal reservoir operation strategy set to calculate the corresponding reduced flood control risk;

[0042] Step S44: The reservoir operation strategy that reduces the flood control risk the most is used as the optimal reservoir operation strategy for the corresponding ultimate flood.

[0043] The risk evolution-based flood control benefit analysis system for reservoir groups in response to extreme climate change includes:

[0044] at least one processor;

[0045] a memory communicatively coupled to at least one of the processors;

[0046] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned risk evolution-based method for analyzing the benefits of reservoir groups in responding to extreme climate floods.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] A risk evolution-based method for analyzing the benefits of reservoir groups in response to extreme climate flood control is adopted to accurately describe the characteristics of extreme climate elements, precisely construct a mapping relationship between them and floods, comprehensively calculate flood control risks, and scientifically construct a mapping relationship between reservoir scheduling and risk reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 It is a flow chart of the present invention.

[0051] Figure 2 It is a flow chart of step S1 of the present invention.

[0052] Figure 3 It is a flow chart of step S2 of the present invention.

[0053] Figure 4It is a flow chart of step S3 of the present invention.

[0054] Figure 5 It is a flow chart of step S4 of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] The present invention will be further explained below with reference to specific embodiments.

[0057] See also Figure 1-Figure 5 The method for analyzing the benefits of flood control for a reservoir group in response to extreme climate events based on risk evolution provided by an embodiment of the present invention includes the following steps:

[0058] Step S1: Collect data from the study area, construct an empirical mode decomposition model and a dynamic three-dimensional meteorological spatial model, screen extreme climate elements and describe their spatiotemporal characteristics;

[0059] Step S2: extract extreme climate elements and input them into a pre-built hydrological model to simulate an initial flood process, build a flood database, extract flood data to train the constructed hydrological model, adjust the model parameters and extract extreme climate elements again as model input to obtain the ultimate flood process;

[0060] Step S3: Collect flood risk data, screen flood risk indicators, build a risk assessment indicator system and a dynamic risk simulation model, extract the ultimate flood process as model input, and calculate the flood control risk corresponding to each ultimate flood;

[0061] Step S4: Collect reservoir scheduling strategies to construct an optimal reservoir scheduling strategy set, input each ultimate flood into the pre-built multi-objective optimization scheduling model in turn, and adopt all reservoir scheduling strategies in the optimal reservoir scheduling strategy set respectively, calculate the corresponding reduced flood control risks respectively, and screen out the optimal reservoir scheduling strategy corresponding to each ultimate flood.

[0062] According to one aspect of the present application, step S1 further comprises:

[0063] Step S11: using a multi-source data fusion acquisition method to collect data from the study area and screen out extreme climate elements;

[0064] Step S12: constructing an empirical mode decomposition model, and constructing a dynamic three-dimensional meteorological spatial model based on the geographic information system by using the long short-term memory network coupled empirical mode decomposition method;

[0065] Step S13: extract extreme climate elements and input them into the empirical mode decomposition model and the dynamic three-dimensional meteorological spatial model respectively to obtain the temporal characteristics and spatial characteristics of each extreme climate element.

[0066] According to one aspect of the present application, step S11 further comprises:

[0067] Step S11 a, using satellite remote sensing equipment to collect temperature and precipitation data in the study area;

[0068] Step S11 b: Collecting refined meteorological data of the study area based on ground IoT devices, where the refined meteorological data includes wind speed, wind direction, and humidity;

[0069] Step S11 c: using a weather radar to collect extreme weather data;

[0070] Step S11 d: Use an adaptive fusion algorithm to calculate the weight of each data source, add the product of multiple data source data of each climate element and its weight as the real meteorological data of the climate element, and screen out climate elements greater than the threshold as extreme climate elements.

[0071] Comprehensive and high-precision acquisition of extreme climate element data covering different temporal and spatial scales provides a rich and accurate data foundation for subsequent in-depth analysis. Existing methods rely solely on conventional meteorological stations and cannot meet the monitoring needs of complex spatiotemporal changes in extreme climate. Although satellite remote sensing can obtain macro information, it lacks local details; ground station data coverage is limited; meteorological radar has advantages in precipitation monitoring, but it is difficult to fully reflect meteorological elements when used alone. Therefore, in this embodiment, a multi-source data fusion method is adopted to integrate the advantages of various data sources, solve the problems of incomplete data acquisition and low spatiotemporal resolution. By fusing different data sources, it is possible to more carefully depict the gradient changes in space and the rapid evolution of extreme climate in time, improve the monitoring and early warning capabilities of extreme climate events, provide more reliable data support for subsequent analysis, and help formulate response strategies in advance to reduce the losses that may be caused by extreme climate.

[0072] According to one aspect of the present application, step S13 is further as follows:

[0073] Step S13a: extract extreme climate factors and input them into the empirical mode decomposition model to obtain multiple intrinsic mode function components and input them into the long short-term memory module for training to obtain the temporal characteristics of the extreme climate factors, including trend changes and periodic characteristics;

[0074] Step S13b: extract extreme climate elements and input them into a dynamic three-dimensional meteorological spatial model, set multiple layers and parameters, and obtain the spatial differences and dynamic changes of extreme climate elements, that is, spatial characteristics.

[0075] Deeply exploring the changing characteristics of extreme climate factors in time and space provides technical means for understanding the evolution of extreme climate and predicting its development trends. Traditional time series analysis methods have difficulty processing the complex nonlinear and non-stationary characteristics of extreme climate data. EMD can adaptively decompose data, and LSTM excels at learning long-term dependencies in time series. The combination of the two can better capture the temporal characteristics of extreme climate. In spatial analysis, traditional two-dimensional maps cannot intuitively display the three-dimensional dynamic relationship between terrain and meteorological elements. Three-dimensional reconstruction technology based on geographic information systems (GIS) can make up for this shortcoming.

[0076] In this embodiment, the problem of insufficient mining of extreme climate data features by traditional analysis methods is solved, and the occurrence time, intensity changes and spatial distribution of extreme climate events under different terrain conditions can be more accurately predicted, providing more forward-looking and targeted information for flood control decision-making, such as arranging flood control materials to be deployed to specific terrain areas prone to disasters in advance.

[0077] According to one aspect of the present application, step S2 further comprises:

[0078] Step S21: construct a distributed hydrological model based on physical processes, and discretize the study area into several calculation units using the finite element method;

[0079] Step S22: extract extreme climate elements and input them into a distributed hydrological model, use Monte Carlo simulation to obtain several initial flood processes, and build a flood database;

[0080] Step S23: extract flood data from the flood database, use the first 80% as a training set and the last 20% as a test set, input the training set together with extreme climate factors and floods into the distributed hydrological model for training, and adjust the model parameters;

[0081] Step S24: using the test set to input the trained distributed hydrological model, evaluating the model based on the simulation results and adjusting the model to obtain an optimized distributed hydrological model;

[0082] Step S25: extract extreme climate elements again and input them into the optimized distributed hydrological model to simulate and obtain several ultimate flood processes.

[0083] In this embodiment, a distributed hydrological model based on physical processes is constructed. The finite element method is used to discretize the watershed into computational units. Through field sampling and laboratory analysis, the spatial variation patterns of soil parameters such as porosity and permeability are obtained. A fractal model is used to describe soil properties. Satellite remote sensing image interpretation and field surveys are used to obtain vegetation type and coverage information. A coupling relationship model between vegetation and hydrological processes is established. At the same time, the Monte Carlo simulation method is introduced to generate a large number of extreme precipitation scenario input models based on historical precipitation statistical characteristics. The runoff production and runoff results under different scenarios are simulated to make the model more realistic.

[0084] Traditional hydrological models are mostly lumped, insufficiently accounting for the spatial heterogeneity of underlying surface conditions within a watershed, and struggle to handle the uncertainties of extreme meteorological conditions. Distributed hydrological models combined with finite element methods can more meticulously depict internal differences within a watershed. Incorporating soil and vegetation factors allows the model to more realistically reflect actual hydrological processes, establishing a hydrological model more consistent with actual watershed conditions. This allows for accurate simulation of flood generation and evolution under extreme climate conditions, providing reliable flood forecast data for flood risk assessment. Monte Carlo simulations can also effectively address precipitation uncertainty.

[0085] This method addresses the low simulation accuracy of traditional hydrological models in extreme climates. It can more accurately predict peak flood flow, total flood volume, and flood duration, providing more accurate flood data for flood control project design and scheduling. This helps rationally plan the layout of flood control facilities and develop scientific flood prevention plans, thereby reducing flood disaster losses.

[0086] A large amount of historical extreme climate and flood data was collected to establish a database. Training data was randomly selected as the training set, and the rest was used as the test set. A deep neural network algorithm was used to train the model using extreme climate factors as input and flood characteristics as output. During training, model parameters were adjusted to optimize performance. The model was then validated using the test set, prediction errors were calculated, and the model was further adjusted to improve accuracy and generalization.

[0087] By using data mining technology, the potential relationship between extreme climate factors and flood characteristics is mined from historical data, and a data-driven prediction model is established to supplement the physical model and improve the reliability of flood prediction. Since traditional models based on physical processes may have limitations when data is insufficient or under complex conditions, data-driven models can make full use of large amounts of historical data, mine complex nonlinear relationships, and have fast training and prediction speeds. Therefore, in this embodiment, a deep neural network is used to construct a mapping relationship model, which solves the problems of traditional models' insufficient ability to handle complex relationships and reliance on too many assumptions. Through data-driven models, the relationship between extreme climate and floods that may be ignored by traditional physical models can be discovered, thereby improving the accuracy and adaptability of flood predictions, especially in the absence of detailed physical parameters or complex boundary conditions, providing a more dimensional reference basis for flood control decisions.

[0088] According to one aspect of the present application, step S3 is further:

[0089] Step S31: Collect flood risk data, screen flood risk indicators, and build a risk assessment indicator system;

[0090] Step S32: construct a dynamic risk simulation model, extract the ultimate flood process as the model input, and calculate the flood control risk corresponding to each ultimate flood.

[0091] According to one aspect of the present application, step S31 is further as follows:

[0092] Step S31a: Collect flood risk data and select ecological environment damage indicators, social stability indicators, and infrastructure restoration cost indicators;

[0093] Step S31b: Use the analytic hierarchy process to construct a judgment matrix, calculate the weights of various flood risk indicators based on expert opinions, and build a risk assessment indicator system.

[0094] Identify indicators of ecological and environmental damage, such as measuring wetland degradation through the rate of change in wetland area and the loss of ecosystem service value. Assess habitat damage based on the proportion of habitat reduction and changes in species diversity indices. Analyze the degree of panic among residents through questionnaires and social media data analysis. Obtain indicators of social order impact from government statistics. Estimate restoration costs for various types of infrastructure based on material, structure, and other parameters, combined with engineering construction and repair cost standards. Finally, use the analytic hierarchy process to construct a judgment matrix, invite experts to score the importance of each indicator, calculate the weight of each indicator, and comprehensively assess multi-dimensional risks.

[0095] Build a comprehensive, multi-dimensional flood risk assessment system that comprehensively considers the impact of floods on the ecological environment, social stability, and infrastructure, and more realistically reflects the overall risk of flood disasters;

[0096] Traditional flood risk assessment focuses mainly on direct economic losses and casualties, ignoring the long-term impact of floods on ecosystems and social stability. This invention incorporates ecological and social indicators to more comprehensively assess the impact of disasters. The analytic hierarchy process can effectively handle the problem of determining the weights of multiple indicators. By combining expert experience and data, the weight distribution is more reasonable, solving the problem of the incompleteness of the traditional assessment system. A comprehensive risk assessment can enable decision makers to have a clearer understanding of the comprehensive impact of flood disasters, focusing not only on immediate economic losses, but also on ecological restoration and social stability, which will help to formulate more sustainable flood control and disaster reduction strategies and promote the harmonious development of man and nature.

[0097] According to one aspect of the present application, step S32 is further as follows:

[0098] Step S32a: construct a dynamic risk simulation model based on the hydrological model, and collect population distribution data, economic value data, and infrastructure distribution data of the study area;

[0099] Step S32b: extract the ultimate flood process and input it into the dynamic risk simulation model, and use Monte Carlo simulation to obtain the flood inundation range, water depth and duration corresponding to different floods;

[0100] Step S32c: For each simulation result, the probability distribution of losses is calculated based on the population distribution data, economic value data, and infrastructure distribution data, and the flood control risk corresponding to each ultimate flood is calculated.

[0101] Based on the flood simulation results of hydrological models or mapping models, combined with population distribution, economic value, and infrastructure distribution, Monte Carlo simulation is used for random simulation. Taking into account the uncertainty of flood evolution, different combinations of flood inundation ranges, water depths, and durations are generated. For each simulation result, the corresponding losses are calculated based on regional data. Through statistical analysis, the loss probability distribution is generated, and the loss expectations for different risk levels are calculated to achieve dynamic and accurate risk assessment.

[0102] This embodiment takes into account the uncertainty factors in the flood evolution process, and through multiple simulations and data analysis, achieves a dynamic and accurate assessment of flood control risks, providing risk information that is more in line with the actual situation for flood control decision-making;

[0103] Traditional risk assessments are mostly static and fail to fully consider the uncertainty of flood evolution, resulting in deviations between assessment results and actual conditions. Monte Carlo simulations can handle uncertainty through a large number of random simulations. Combined with detailed regional data, they can more realistically reflect the losses under different risk scenarios.

[0104] This method solves the static and inaccurate problems of traditional risk assessment. Dynamic risk simulation can provide decision makers with the distribution of loss possibilities under different risk levels, helping them to formulate more flexible and effective flood control measures, rationally allocate flood control resources, and improve the ability to respond to flood disasters.

[0105] According to one aspect of the present application, step S4 is further:

[0106] Step S41: Construct a multi-objective optimization scheduling model, wherein the flood control objective is based on the degree of flood risk reduction as a measurement indicator, the power generation objective is based on maximizing power generation as an objective function, the water supply objective is based on meeting downstream water demand as a constraint condition, and the ecological objective is based on maintaining the ecological base flow of the river;

[0107] Step S42: Input each ultimate flood into the multi-objective optimization scheduling model in sequence, use the non-dominated sorting genetic algorithm to solve the model, screen out the optimal reservoir scheduling strategy, and construct the optimal reservoir scheduling strategy set;

[0108] Step S43: input each ultimate flood into the multi-objective optimization scheduling model in turn and respectively adopt all reservoir scheduling strategies in the optimal reservoir scheduling strategy set to calculate the corresponding reduced flood control risk;

[0109] Step S44: The reservoir operation strategy that reduces the flood control risk the most is used as the optimal reservoir operation strategy for the corresponding ultimate flood.

[0110] Construct an optimization scheduling model with multiple objectives including flood control, power generation, water supply, and ecology. Flood control is based on the degree of risk reduction, power generation is based on the objective function of maximizing power generation, water supply meets downstream water demand as a constraint, and ecology maintains the ecological base flow of the river as an indicator. Engineering constraints such as upper and lower limits of reservoir water levels, flood discharge, and water storage capacity are determined. A non-dominated sorting genetic algorithm is then used to solve the problem while satisfying the constraints. The optimal scheduling strategy set is searched through operations such as population initialization, selection, crossover, and mutation. Based on the objective priority and weight optimization strategy, the relationship between each objective is weighed to determine a scheduling plan that takes into account flood control and other benefits. A scientific and reasonable reservoir scheduling plan is formulated to effectively reduce flood control risks while taking into account other important functions such as power generation, water supply, and ecology, thereby maximizing the comprehensive benefits of the reservoir group.

[0111] Traditional reservoir scheduling often focuses on a single flood control goal and ignores the multifunctionality of reservoirs. Multi-objective optimization models can comprehensively consider multiple goals. Non-dominated sorting genetic algorithms can search for optimal solutions in complex solution spaces. By determining target weights and priorities, they achieve a balance among various goals, solving the problem that traditional reservoir scheduling has a single target and cannot fully realize the comprehensive benefits of reservoirs. Multi-objective optimization scheduling schemes can flexibly adjust reservoir operation modes according to actual needs in different periods. While ensuring flood control safety, they can improve power generation efficiency, meet water supply needs, and protect the ecological environment, thereby improving the overall operating efficiency and economic benefits of the reservoir group.

[0112] According to another aspect of the present application, a risk evolution-based flood control benefit analysis system for a reservoir group in response to extreme climate is provided, comprising:

[0113] at least one processor; and

[0114] a memory communicatively connected to at least one of the processors; wherein,

[0115] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement any of the above-mentioned risk evolution-based methods for analyzing the benefits of reservoir groups in response to extreme climate floods.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A risk evolution-based method for analyzing the benefits of reservoir groups in response to extreme climate floods, characterized by: The method comprises the following steps: Step S1: Collect data from the study area, construct an empirical mode decomposition model and a dynamic three-dimensional meteorological spatial model, screen extreme climate elements and describe their spatiotemporal characteristics; Step S2: extract extreme climate elements and input them into the pre-built hydrological model to simulate the initial flood process, build a flood database, train and adjust the hydrological model parameters, and then input extreme climate elements again to obtain the ultimate flood process; Step S3: Collect flood risk data, build a risk assessment indicator system and a dynamic risk simulation model, and input the ultimate flood process to calculate flood control risks; Step S4: Construct the optimal reservoir operation strategy set, input the ultimate flood into the multi-objective optimization operation model, calculate the flood control risk reduced by different operation strategies, and screen the optimal operation strategy.

2. The risk evolution-based benefit analysis method for reservoir groups in response to extreme climate floods according to claim 1 is characterized in that: The step S1 specifically includes: Step S11: using multi-source data fusion method to collect data of the study area and screen extreme climate elements; Step S12: constructing an empirical mode decomposition model and a GIS-based long short-term memory network coupled dynamic three-dimensional meteorological spatial model; Step S13: Input extreme climate elements to obtain temporal characteristics and spatial characteristics respectively.

3. The risk evolution-based benefit analysis method for reservoir groups in response to extreme climate floods according to claim 2 is characterized in that: The multi-source data fusion in step S11 includes: Step S11 a: Satellite remote sensing collects temperature and precipitation data; Step S11 b: The ground IoT device collects wind speed, wind direction and humidity data; Step S11 c: weather radar collects extreme weather data; Step S11 d: Adopting an adaptive fusion algorithm to calculate the weight of each data source and filter extreme climate elements.

4. The method for analyzing the benefits of reservoir groups in response to extreme climate floods based on risk evolution according to claim 2 is characterized in that: The step S13 specifically includes: Step S13a: Time features are obtained through empirical mode decomposition model and long short-term memory network training, including trend changes and periodic features; Step S13b: spatial features are obtained by setting multi-layer parameters of the dynamic three-dimensional meteorological spatial model, including spatial differences and dynamic changes.

5. The method for analyzing the benefits of reservoir groups in response to extreme climate floods based on risk evolution according to claim 2 is characterized in that: The step S2 specifically includes: Step S21: construct a distributed hydrological model based on physical processes and discretize the study area into several calculation units using the finite element method; Step S22: extract extreme climate elements and input them into the distributed hydrological model, use Monte Carlo simulation to obtain several initial flood processes, and build a flood database; Step S23: extract flood data from the flood database, use the first 80% as a training set and the last 20% as a test set, input the training set together with extreme climate factors and floods into the distributed hydrological model for training, and adjust the model parameters; Step S24: using the test set to input the trained distributed hydrological model, evaluating the model based on the simulation results and adjusting the model to obtain an optimized distributed hydrological model; Step S25: extract extreme climate elements again and input them into the optimized distributed hydrological model to simulate and obtain several ultimate flood processes.

6. The risk evolution-based benefit analysis method for reservoir groups in response to extreme climate floods according to claim 1 is characterized in that: The risk assessment indicator system construction in step S3 includes: Step S31: Collect flood risk data, screen flood risk indicators, and build a risk assessment indicator system; Step S32: Construct a dynamic risk simulation model, extract the ultimate flood process as the model input, and calculate the flood control risk corresponding to each ultimate flood.

7. The risk evolution-based benefit analysis method for reservoir groups in response to extreme climate floods according to claim 6 is characterized in that: The step S31 specifically includes: Step S31a: Collect flood risk data and select ecological environment damage indicators, social stability indicators and infrastructure restoration cost indicators respectively; Step S31 b: Use the analytic hierarchy process to construct a judgment matrix, calculate the weights of each flood risk indicator based on expert opinions, and build a risk assessment indicator system.

8. The risk evolution-based benefit analysis method for reservoir groups in response to extreme climate floods according to claim 6 is characterized in that: The step S32 specifically includes: Step S32a: construct a dynamic risk simulation model based on the hydrological model, and collect population distribution data, economic value data, and infrastructure distribution data of the study area; Step S32b: extracting the ultimate flood process and inputting it into the dynamic risk simulation model, and using Monte Carlo simulation to obtain the flood inundation range, water depth, and duration corresponding to different floods; Step S32c: For each simulation result, the probability distribution of losses is calculated based on the population distribution data, economic value data and infrastructure distribution data, and the flood control risk corresponding to each ultimate flood is calculated.

9. The risk evolution-based flood control benefit analysis method for reservoir groups in response to extreme climate conditions according to claim 1 is characterized in that: The step S4 specifically includes: Step S41: Construct a multi-objective optimization scheduling model, in which the flood control objective is measured by the degree of flood risk reduction, the power generation objective is based on maximizing power generation as the objective function, the water supply objective is based on meeting downstream water demand as a constraint condition, and the ecological objective is based on maintaining the ecological base flow of the river; Step S42: input each ultimate flood into the multi-objective optimization operation model in sequence, use the non-dominated sorting genetic algorithm to solve the model, screen out the optimal reservoir operation strategy, and construct the optimal reservoir operation strategy set; Step S43: input each ultimate flood into the multi-objective optimization operation model in turn and respectively adopt all reservoir operation strategies in the optimal reservoir operation strategy set to calculate the corresponding reduced flood control risk; Step S44: The reservoir operation strategy that reduces the flood control risk the most is used as the optimal reservoir operation strategy for the corresponding ultimate flood.

10. The risk evolution-based reservoir group flood control benefit analysis system for extreme climate is characterized by: include: at least one processor; a memory communicatively coupled to at least one of the processors; In which, the memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the risk evolution-based flood control benefit analysis method for a reservoir group in response to extreme climate as described in any one of claims 1-9.

Images