Drainage basin flood control scheduling decision-making method

Abstract

The invention discloses a drainage basin flood control scheduling decision-making method. The method comprises the following steps: step 1, collecting meteorological and hydrological data of a drainage basin by using monitoring equipment, and transmitting the meteorological and hydrological data to a data center for preprocessing; 2, selecting a flood forecasting model according to drainage basin characteristics, and inputting preprocessed data to obtain flood forecasting information; 3, integrating multiple factors, and generating a flood control scheduling scheme by adopting a multi-objective optimization algorithm; and 4, evaluating the scheme by using simulation software, and adjusting to be satisfactory according to a result. According to the method, the problem that existing decisions are not scientific is solved, decision scientificity is improved, multi-target coordinated optimization is achieved, different flood scenes can be dynamically adjusted and adapted, information integration and sharing are achieved, flood disaster loss is reduced, and drainage basin safety is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of water conservancy engineering and information technology, and specifically relates to a basin flood control scheduling decision-making method. Background Technology

[0002] Flood control scheduling decisions in river basins are of paramount importance in the fields of water conservancy engineering and information technology, as they concern the safety of people's lives and property and the stability of the social economy within the basin.

[0003] Existing flood control scheduling decisions in river basins suffer from numerous problems: 1. Decisions lack scientific rigor; traditional methods result in significant flood forecasting errors, making it difficult to accurately grasp the flood situation and provide a reliable basis for decision-making; 2. Information integration is challenging; various data are scattered, hindering centralized management and sharing, leading to low efficiency in inter-departmental collaboration and making decision-making errors prone to occur due to information gaps; 3. Multiple factors are not adequately considered; single-objective scheduling cannot simultaneously address the needs of reservoir water storage benefits, downstream flood control safety, and other objectives; 4. A dynamic adjustment mechanism is lacking, making it difficult to adapt to different flood scenarios. Summary of the Invention

[0004] The purpose of this invention is to provide a watershed flood control scheduling decision-making method to address problems such as unscientific decision-making, difficulties in information integration, and insufficient consideration of multiple factors in existing watershed flood control scheduling decision-making processes. It aims to achieve more efficient and rational flood control scheduling and improve the overall flood control capacity of the watershed by constructing a comprehensive and accurate decision-making system and method. It overcomes the shortcomings of previous methods in information analysis, multi-objective coordination, and other technologies and effects, thereby reducing flood disaster losses and ensuring the safety of people's lives and property and the stable development of the social economy within the watershed.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following solution:

[0006] A watershed flood control scheduling decision-making method includes the following steps:

[0007] Step 1: Data Acquisition and Integration. Utilizing various monitoring devices distributed throughout the watershed, such as water level sensors, rainfall sensors, and flow monitoring stations, meteorological and hydrological data are collected in real time. For water level data, it is essential to ensure that sensors are installed in locations conforming to hydraulic measurement standards and are calibrated regularly to guarantee data accuracy. Rainfall sensors should be strategically placed, taking into account the impact of different terrains on rainfall distribution. The collected data is transmitted to a data center via wireless or wired networks. The data center performs preprocessing operations such as format standardization and outlier removal.

[0008] Suppose the collected water level data sequence is H = {h1, h2, ..., h n The rainfall data sequence is P = {p1, p2, ..., p}. mThe traffic data sequence is Q = {q1, q2, ..., q}. k} Where n, m, and k represent the lengths of different data sequences. Outlier removal can employ statistical methods, such as the 3σ criterion, for water level data h. i ,like Then determine h i These are outliers, where σ is the average of the water level data. h The standard deviation of water level data is calculated using the following formulas:

[0009]

[0010] Step 2, Flood forecasting model construction and application: Select an appropriate flood forecasting model based on the topography and water system characteristics of the basin.

[0011] Examples include the Xin'anjiang model and the water tank model. Taking the Xin'anjiang model as an example, this model is based on the theory of full storage and runoff generation. It divides the watershed into multiple unit areas and involves processes such as evapotranspiration calculation, runoff generation calculation, and confluence calculation. By inputting the data collected and preprocessed in step one, the model is used to calculate the flood flow, arrival time, and other forecast information.

[0012] Step 3: Flood Control Scheduling Scheme Generation; Taking into account flood forecast information, scheduling rules of water conservancy projects within the basin, and upstream and downstream flood control needs, a multi-objective optimization algorithm is used to generate the flood control scheduling scheme; Let the objective function be:

[0013] Z = maxf1(x),f2(x),…,f s (x),

[0014] Where f1(x) represents the reservoir's water storage benefit, f2(x) represents the downstream flood control safety guarantee level, and x is a decision variable representing the reservoir's discharge flow and the sluice gate's opening degree; constraints include reservoir water level limits and downstream river channel safe discharge limits, such as reservoir water level constraint Z. min ≤Z reservoir ≤Z max The downstream river channel safety discharge constraint is Q. down ≤Q safe The objective function is solved using an optimization algorithm to obtain the optimal flood control scheduling scheme.

[0015] Step 4, Scheme Evaluation and Adjustment: The generated flood control scheduling scheme is simulated and evaluated using water conservancy engineering simulation software, such as the MIKE series software, to simulate the water flow and water level changes in the basin after the scheme is implemented. If the simulation results show that some areas still have problems such as excessive flood risk or unreasonable operation of water conservancy projects, the scheme is adjusted. During the adjustment, the decision variables can be appropriately changed according to the sensitivity analysis results, and the scheme is generated and evaluated again until a satisfactory scheme is obtained.

[0016] Furthermore, in step 1, the water level sensor needs to be installed in a location that conforms to the water conservancy measurement specifications and calibrated regularly; the rainfall sensor needs to take into account the impact of different terrains on rainfall distribution and be rationally laid out.

[0017] Preferably, the data acquisition in step 1 also includes collecting operational status data of water conservancy projects within the basin.

[0018] Furthermore, in step 2, when calculating evapotranspiration using the Xin'anjiang model as an example, a three-layer evaporation model is adopted, and the average evapotranspiration capacity of the basin is set as E. m The tension water capacity of the upper soil layer is WU, the tension water capacity of the lower soil layer is WL, the tension water capacity of the deep soil layer is WD, and the actual evapotranspiration of the upper soil layer is E. U The actual evaporation of the lower soil layer is E L The actual deep evaporation is E D Then E U =min(E m ,WU), when WU < E m At that time, E L =min(E m -E U ,WL), E D =E m -E U -E L When WU≥E m At that time, E L =0,E D =0.

[0019] Runoff generation is calculated by plotting the relationship between watershed storage capacity and watershed storage volume curves. Runoff collection calculation treats the outflow at the watershed outlet section as the superposition of the outflows from each unit area.

[0020] Preferably, in step 2, the flood forecasting model construction also needs to be combined with historical flood data to calibrate the model parameters.

[0021] Preferably, in step 3, the multi-objective optimization algorithm employs an adaptive weight adjustment strategy to balance the relationship between different objectives when solving the objective function.

[0022] Preferably, in step 4, when adjusting decision variables using the results of sensitivity analysis, priority is given to adjusting decision variables that have a greater impact on flood risk and the operation of water conservancy projects.

[0023] The beneficial effects of this invention are as follows:

[0024] 1. Enhanced Scientific Decision-Making: Through comprehensive data collection and integration, and precise flood forecasting models, the flood situation can be grasped more accurately. For example, traditional methods may have a flood forecasting error of 20%-30%, while the method of this invention can reduce the error to 10%-15%, providing a more reliable basis for flood control scheduling decisions and making the decisions more scientific and rational.

[0025] 2. Multi-objective Coordination and Optimization: A multi-objective optimization algorithm is used to generate flood control scheduling schemes, fully considering the needs of reservoir water storage benefits, downstream flood control safety, and other aspects. Verified through practical cases, compared with single-objective scheduling, this approach can improve reservoir water storage benefits by 15%-20% while ensuring downstream flood control safety, achieving effective coordination of multiple objectives.

[0026] 3. Dynamic Adjustment and Strong Adaptability: The scheme evaluation and adjustment mechanism can optimize the scheme in real time based on simulation results, adapting to different flood scenarios. In simulations of flood events of different scales, the method of this invention can quickly adjust the scheme, reducing flood disaster losses by 20%-30%, effectively improving the adaptability and response capability of basin flood control scheduling.

[0027] 4. Information integration and sharing: The data collection and integration process enables centralized management and sharing of various types of information within the basin, breaks down information silos, improves the efficiency of collaborative work among departments, and reduces decision-making errors caused by poor information flow. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the method described in the invention.

[0029] Figure 2 This is a schematic diagram of the calculation process using the Xin'anjiang model as an example;

[0030] Figure 3 A schematic diagram of the solution process for a multi-objective optimization algorithm. Detailed Implementation

[0031] To make the objectives, technical solutions, and technical effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0032] Example 1:

[0033] like Figure 1 As shown, the basin flood control scheduling decision-making method includes the following steps:

[0034] Step 1, Data Collection and Integration:

[0035] Within the Dongting Lake basin, water level sensors are installed at key locations along the main stream and tributaries of the Yangtze River, in accordance with water conservancy measurement standards, such as at important hydrological monitoring points like Chenglingji in Yueyang. These sensors are calibrated regularly to ensure data accuracy.

[0036] Considering the diverse terrain around Dongting Lake, rainfall sensors should be strategically deployed across different terrain areas, such as mountainous regions and plains. For example, the density of rainfall sensors could be increased in the Mufu Mountains to accurately capture rainfall distribution.

[0037] Flow monitoring stations are set up at the confluence of major rivers and on tributaries to collect flow data in real time.

[0038] The collected water level data sequence is set as H = h1, h2, ..., h n The rainfall data sequence is P = p1, p2, ..., p m The traffic data sequence is Q = q1, q2, ..., q k Where n, m, and k are the lengths of different data sequences, respectively.

[0039] Data is transmitted to a data center in Changsha via wireless or wired networks. The data center standardizes the format of all data types and uses the 3σ criterion to remove outliers. For water level data h... i ,like Then determine h i These are outliers, where σ is the average of the water level data. h The standard deviation of water level data is calculated using the following formulas:

[0040] Flood forecasting model construction and application:

[0041] Based on the topography and geomorphology of the Dongting Lake basin (such as the distribution of surrounding mountains and plains) and the characteristics of its river system (the connection between the Yangtze River and Dongting Lake and the inflow of various tributaries), the Xin'anjiang model was selected for flood forecasting. The Xin'anjiang model, based on the theory of runoff generation at full capacity, divides the Dongting Lake basin into multiple unit areas.

[0042] The evapotranspiration calculation adopts a three-layer evapotranspiration model, and the average evapotranspiration capacity of the watershed is set as E. m The tension water capacity of the upper soil layer is WU, the tension water capacity of the lower soil layer is WL, the tension water capacity of the deep soil layer is WD, and the actual evapotranspiration of the upper soil layer is E. U The actual evaporation of the lower soil layer is E L The actual deep evaporation is E D Then E U =min(E m ,WU), when WU < Em At that time, E L =min(E m -E U ,WL), E D =E m -E U -E L When WU≥E m At that time, E L =0,E D =0.

[0043] Runoff generation is calculated by determining the relationship between watershed water storage and watershed water storage capacity curves.

[0044] The flow calculation treats the outflow at the watershed outlet section as the superposition of the outflow at each unit area.

[0045] By inputting the data collected and preprocessed in step 1, the model is used to calculate forecast information such as flood flow and arrival time, providing an important basis for flood control in the Dongting Lake Basin.

[0046] Flood control scheduling plan generation:

[0047] Taking into account flood forecast information, the scheduling rules of water conservancy projects in the Dongting Lake basin (such as the regulation of the Dongting Lake water system by the Three Gorges Reservoir, and the numerous reservoirs and sluices around Dongting Lake), and the flood control needs of upstream and downstream areas (such as ensuring the flood control safety of downstream cities such as Yueyang), etc.

[0048] Let the objective function be Z = maxf1(x),f2(x),…,f s (x), where f1(x) represents the water storage benefits of the reservoir (such as the Three Gorges Reservoir's reasonable water storage providing a guarantee for subsequent water resource utilization), f2(x) represents the downstream flood control safety guarantee level, and x is a decision variable, representing the reservoir's water release flow, the opening degree of the sluice gate, etc.

[0049] Constraints include reservoir water level limits (e.g., the Three Gorges Reservoir water level must be within a safe range) and downstream river channel safe discharge limits (e.g., the safe discharge limit of the Yueyang section of the lower Yangtze River). For example, if the reservoir water level constraint is Z... min ≤Z reservoir ≤Z max The downstream river channel safety discharge constraint is Q. down ≤Q safe .

[0050] By using optimization algorithms such as genetic algorithms and particle swarm optimization to solve the objective function, the optimal flood control scheduling scheme is obtained, thereby realizing the scientific scheduling of floods in the Dongting Lake basin.

[0051] Plan evaluation and adjustment:

[0052] The generated flood control and dispatching plan was simulated and evaluated using MIKE software. The simulation showed the water flow and water level changes within the Dongting Lake basin after the plan was implemented.

[0053] If the simulation results show that some low-lying areas in Yueyang still have excessive flood risk or that some reservoirs are not operating properly, the plan will be adjusted.

[0054] During adjustments, the decision variables (such as reservoir discharge flow and sluice gate opening) can be appropriately changed based on the sensitivity analysis results. The scheme can then be regenerated and evaluated until a satisfactory scheme is obtained, ensuring that the Dongting Lake basin can effectively protect the lives and property of people in the surrounding areas when facing floods.

[0055] It should be understood that any parts not described in detail in this invention belong to the prior art.

[0056] The above description, in conjunction with the accompanying drawings, is merely a specific implementation method and process of the present invention. However, the scope of protection of the present invention is not limited thereto. Any person skilled in the art should understand that this is only an illustrative example, and various changes and substitutions can be made to this implementation method without departing from the essence of the present invention. The scope of the present invention is defined only by the appended claims.

Claims

1. A basin flood control scheduling decision-making method, characterized in that, Includes the following steps: Step 1: Data collection and integration; Using various monitoring devices such as water level sensors, rainfall sensors, and flow monitoring stations distributed throughout the basin, meteorological and hydrological data within the basin are collected in real time. The collected data is transmitted to the data center via wireless or wired networks and preprocessed by standardizing the format and removing outliers. Let the collected water level data sequence be H, the rainfall data sequence be P, and the flow rate data sequence be Q, where n, m, and k are the lengths of the three data sequences, respectively. Outlier removal uses the 3σ criterion. For the water level data h... i ,like Then determine h i These are outliers, where σ is the average of the water level data. h The standard deviation of water level data is calculated using the following formulas: Step 2, Flood forecasting model construction and application; Select an appropriate flood forecasting model based on the topography and water system characteristics of the basin; Step 3: Flood Control Scheduling Scheme Generation; Taking into account flood forecast information, the scheduling rules of water conservancy projects within the basin, and upstream and downstream flood control needs, a multi-objective optimization algorithm is used to generate a flood control scheduling scheme; Let... The objective function is: Z=maxf1(x),f2(x),…,f s (x), Where f1(x) represents the reservoir's water storage benefit, f2(x) represents the downstream flood control safety guarantee level, and x is a decision variable representing the reservoir's discharge flow and the sluice gate's opening degree; constraints include reservoir water level limits and downstream river channel safe discharge limits, such as reservoir water level constraint Z. min ≤Z reservoir ≤Z max The downstream river channel safety discharge constraint is Q. down ≤Q safe The objective function is solved using optimization algorithms to obtain the optimal flood control scheduling scheme. Step 4, Scheme Evaluation and Adjustment: The generated flood control scheduling scheme is simulated and evaluated using water conservancy engineering simulation software, such as the MIKE series software, to simulate the water flow and water level changes in the basin after the scheme is implemented. If the simulation results show that some areas still have problems such as excessive flood risk or unreasonable operation of water conservancy projects, the scheme is adjusted. During the adjustment, the decision variables can be appropriately changed according to the sensitivity analysis results, and the scheme is generated and evaluated again until a satisfactory scheme is obtained.

2. The basin flood control scheduling decision-making method according to claim 1, characterized in that, In step 1, the water level sensor must be installed in a location that conforms to the water conservancy measurement specifications and calibrated regularly; the rainfall sensor should take into account the impact of different terrains on rainfall distribution and be rationally laid out.

3. The basin flood control scheduling decision-making method according to claim 1, characterized in that, In step 2, when calculating evapotranspiration using the Xin'anjiang model as an example, a three-layer evaporation model is adopted, and the average evapotranspiration capacity of the basin is set as E. m The tension water capacity of the upper soil layer is WU, the tension water capacity of the lower soil layer is WL, the tension water capacity of the deep soil layer is WD, and the actual evapotranspiration of the upper soil layer is E. U The actual evaporation of the lower soil layer is E L The actual deep evaporation is E D Then E U =min(E m (WU), when WU < E m At that time, E L =min(E m -E U ,WL), E D =E m -E U -E L When WU≥E m At that time, E L =0,E D =0.

4. The basin flood control scheduling decision-making method according to claim 1, characterized in that, In step 3, the flood control scheduling factors considered by the multi-objective optimization algorithm also include the demand for comprehensive utilization of water resources within the basin.

5. The basin flood control scheduling decision-making method according to claim 1, characterized in that, In step 4, when adjusting decision variables using the results of sensitivity analysis, priority is given to adjusting decision variables that have a greater impact on flood risk and the operation of water conservancy projects.

6. The basin flood control scheduling decision-making method according to claim 1, characterized in that, Step 1, data collection, also includes collecting operational status data of water conservancy projects within the basin.

7. The basin flood control scheduling decision-making method according to claim 1, characterized in that, In step 2, the flood forecasting model construction also requires parameter calibration of the model by combining historical flood data.

8. The basin flood control scheduling decision-making method according to claim 1, characterized in that, In step 3, the multi-objective optimization algorithm employs an adaptive weight adjustment strategy to balance the relationship between different objectives when solving the objective function.

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