CN106875045B - Basin optimal management measure optimization method considering block topological relation - Google Patents
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Abstract
The invention discloses a basin optimal management measure optimization method considering a topological relation of a land, which comprises the following steps: (1) integrally dividing the drainage basin into a plurality of plots and constructing a plot tree; (2) initializing to generate a population with a certain quantity of scales, and determining BMP corresponding to each plot in an individual through a space interaction rule; (3) performing non-dominant sorting according to the silt reduction rate and the cost, and performing cross variation on every two sorted pairs so as to generate a next generation population; (4) and continuously carrying out genetic evolution to obtain the optimal BMP solution of the whole watershed. The invention fully utilizes the upstream and downstream relation of the basin management unit (namely the plot) and the knowledge of the spatial interaction of the BMP to carry out population initialization, and compared with the traditional method of generating an initial solution by a random method, the invention can improve the quality of the solution, is beneficial to the convergence of an algorithm and quickly finds a better solution.
Description
Technical Field
The invention belongs to the technical field of agricultural non-point source pollution simulation technology and optimal management measure optimization, and particularly relates to a drainage basin optimal management measure optimization method considering a block topological relation.
Background
Best management measures (BMP) are a series of measures taken to control and reduce non-point source pollution, prevent water and soil loss, protect soil resources, and improve water quality and the ecological environment of a drainage basin. When BMP is selected and optimally configured in an actual watershed, generally, the selection of measures of an upstream management unit is determined to some extent by the selection of measures of a downstream unit, for example, when the downstream management unit implements measures of a lawn filter belt, the upstream management unit does not need to implement high-level tillage or no-tillage measures, and therefore, BMP optimization should take into account the upstream and downstream relationship of the management unit.
The existing BMP optimization methods comprise two methods, one is that a BMP scheme is designed by considering the upstream and downstream relations of a management unit to a certain extent according to expert experience and key source area identification, and then preferential selection is carried out through basin model simulation evaluation, but a semi-distributed basin model is mostly adopted during evaluation of the scheme, and the spatial interaction of the management unit in the scheme cannot be simulated (for example, the influence of upstream pollution discharge on the downstream); the other method is to optimize the basin BMP space configuration scheme based on an intelligent optimization algorithm, but because the optimization algorithm generates an initial population (initial solution) by a random method and guides to generate a new population (new solution) to optimize the BMP configuration according to a model evaluation result, a used model mostly adopts a semi-distributed basin model in the existing research, and the adopted random method also ignores the upstream and downstream relation of a management unit. In summary, the two existing BMP optimization methods do not fully consider the upstream and downstream relationships of the management unit and the spatial interaction thereof.
When BMP space optimization configuration is carried out in the flow domain based on an optimization algorithm, if the upstream and downstream relation of a management unit and the prior knowledge of the space interaction of management measures are considered for optimization, and a fully distributed flow domain model capable of reflecting the material interaction between the space units is adopted as a BMP evaluation model, the convergence speed of the optimization algorithm can be improved, and the quality of an optimal solution can be improved.
Disclosure of Invention
In view of the above, the present invention provides a watershed optimal management measure optimization method considering a topological relation of a block, which applies existing spatial interaction knowledge of the watershed optimal management measure to an optimization algorithm initial so as to improve a starting point of algorithm search.
A method for optimizing management measures of a drainage basin by considering a topological relation of a land parcel comprises the following steps:
(1) integrally dividing the basin into a plurality of independently managed plots with upstream and downstream relations, and constructing a plot tree, wherein the plots have a single land utilization type and a definite upstream and downstream relation;
(2) initializing to generate a population with a certain number of scales, wherein each individual in the population is a set of potential watershed overall management scheme, namely, the population corresponds to a plot tree, and each plot determines a corresponding BMP through a spatial interaction rule;
(3) sequencing individuals in the population by using a rapid non-dominant sequencing method according to the sediment reduction rate and the cost, and carrying out cross variation on pairwise pairing of the individuals according to the sequence so as to generate a next generation population;
(4) and (4) continuously carrying out genetic evolution according to the step (3) until a set iteration termination condition is reached, finally obtaining a new generation of population as an elite set, and further selecting an individual from the elite set as an optimal management scheme of the whole basin according to an environmental target and budget for basin governance.
The specific process of the step (1) is as follows:
1.1 establishing a whole flow chart of the drainage basin by utilizing a DEM (digital elevation model);
1.2 correspondingly taking all grids in the flow graph as nodes to build a tree structure according to the flow direction relation;
1.3 according to the land utilization graph, combining the nodes with the same land utilization type and direct flow direction relation in the tree structure, wherein the combined tree structure is the plot tree, and each node corresponds to the plot.
The specific process of the step (2) is as follows:
2.1 selecting a proper BMP for the root node or randomly generating the BMP of the root node by taking the plot where the drainage basin outlet is positioned as the root node of the plot tree according to the land utilization type;
2.2 for any plot except the root node in the plot tree, if the BMP of the downstream plot flowing directly is taken as an unforeseen measure, randomly selecting one of the unforeseen measures and other concrete measures as the BMP of the plot; if the BMP of the downstream plot which is directly flowed to is a specific measure, the BMP of the plot is made to be a non-measure; determining BMP of each plot in the plot tree according to the determined BMP, and obtaining an individual;
2.3 performing a plurality of times according to steps 2.1 and 2.2, generating a plurality of individuals, thereby forming a population.
The calculation expression of the individual silt reduction rate in the step (3) is as follows:
wherein: fsed(X) represents the sand reduction rate of the whole watershed after the implementation according to the block BMPs in the individual X, V (0) is the sand analog quantity generated by the erosion of the whole watershed under the condition that no measure is taken by each block, and V (X) is the sand analog quantity generated by the erosion of the whole watershed under the implementation condition according to the block BMPs in the individual X.
The calculation expression of the individual cost in the step (3) is as follows:
wherein: fcost(X) represents the total cost of BMP implementation per individual block of individual X, C (X)i) Denotes the BMP implementation cost per unit area of the ith plot in the individual X, AiDenotes the area of the ith plot and n is the total number of plots.
The specific process of enabling the individuals to pair pairwise for cross variation in the step (3) is as follows: for any pair of individuals, arbitrarily selecting a land parcel a from one of the individuals A except for the root node, and simultaneously selecting a land parcel B from the other individual B, wherein the land parcel B and the land parcel a are in the same position in the respective individuals; a subtree taking the land parcel a as a root node in the individual A and a subtree taking the land parcel B as a root node in the individual B are exchanged with each other to obtain an individual A 'and an individual B', so that the crossing process is completed; and for the new individuals obtained after crossing, randomly selecting a plurality of individuals from the new individuals, randomly selecting a plot from the selected new individuals, and randomly selecting a specific measure or taking no measures to replace the original BMP of the plot, thereby completing the mutation process.
The invention fully utilizes the upstream and downstream relation of the basin management unit (namely the plot) and the knowledge of the spatial interaction of the BMP to carry out population initialization, and compared with the traditional method of generating an initial solution by a random method, the invention can improve the quality of the solution, is beneficial to the convergence of an algorithm and quickly finds a better solution.
Drawings
FIG. 1 is a schematic diagram of the optimization process of the method of the present invention.
FIG. 2 is a schematic flow chart of land parcel division in the present invention.
Fig. 3 is a schematic flow chart of population initialization in the present invention.
FIG. 4 is a schematic diagram of the crossover strategy of the genetic algorithm of the present invention.
FIG. 5 is a schematic diagram showing the comparison of the experimental steps of the method of the present invention and the conventional method.
FIG. 6(a) is a graph comparing the experimental results of the method of the present invention and the conventional method at a population size of 60.
FIG. 6(b) is a graph comparing the experimental results of the method of the present invention and the conventional method in the case where the population size is 100.
FIG. 6(c) is a graph comparing the experimental results of the method of the present invention and the conventional method at a population size of 200.
Detailed Description
In order to more specifically describe the present invention, the following detailed description is provided for the technical solution of the present invention with reference to the accompanying drawings and the specific embodiments.
The method of the invention is based on genetic algorithm, takes basin simulation technology as means, combines the existing basin best management measure BMP space interaction knowledge, fully considers the space topological relation of the management unit, namely the upstream and downstream relation, and carries out BMP optimization, and the method comprises the following specific steps as shown in figure 1:
(1) the method comprises the following steps of integrally dividing a basin into basin management units with upstream and downstream relations, and taking a land parcel as a management unit, providing a new land parcel dividing method: according to the land use and the flow diagram extracted by the DEM, the land parcel is divided again, and the divided land parcel has a single land use type and a definite upstream-downstream relationship; the specific operation of this step is shown in fig. 2:
1.1, constructing a flow direction relation tree structure according to a flow diagram, and simultaneously recording the ID number (ID corresponds to a space position) of each grid in the tree structure;
1.2, overlapping the ID number of each grid of the land use map with grids with the same ID in the tree structure to form a tree structure with land use information and flow direction relation;
1.3 combining the grids of the same land utilization to finally form a land block with upstream and downstream relation and single land utilization.
(2) And selecting a multi-target genetic algorithm (such as epsilon-NSGA-II), and constructing a distributed watershed model (such as a Landscape SWAT model) and a BMP cost calculation component.
(3) Extracting space interaction rules among optimal management measures of the drainage basin BMP, and applying the space interaction rules to a population initialization process of a genetic algorithm, namely generating a certain amount of BMP space configuration scenes on a plot according to the rules; the specific operation of this step is shown in fig. 3:
3.1 reading the plot information with the upstream and downstream relation to construct a plot tree;
3.2 finding out a plot (root node of a tree structure) where the drainage basin outlet is located, wherein the plot is a downstream plot of other plots, and randomly generating or assigning a certain appropriate BMP according to the land utilization type of the plot;
3.3 starting from the child node of the root node, traversing each land block according to the relationship between the land blocks from the downstream to the upstream (the tree is traversed in the first order), firstly judging whether the downstream land block implements the BMP, if so, not implementing the BMP, and if not, randomly generating or appointing a proper BMP according to the land utilization type of the land block. Suppose there are 3 alternative BMPs, respectively: without measures, no tillage, returning to the crop, we have the BMP interaction rules shown in table 1:
TABLE 1
And 3.4, generating a population according to the population scale (number of individuals) specified by the user and the steps 3.2-3.3.
(4) And (4) multi-target evaluation (individual fitness calculation), namely, calling the basin model and the cost calculation component to calculate a plurality of targets (such as sand reduction rate maximum and cost minimum) of each BMP scene. Wherein the objective of the multi-objective evaluation comprises an environmental objective and an economic objective, and the optimization problem is described as maximizing the silt reduction rate and minimizing the BMP cost; when BMP scene multi-target evaluation is carried out, a new scene generated by an optimization algorithm needs to be compared with a reference scene (baseline scene), wherein the reference scene refers to a model simulation result when BMP is not implemented at present; the model simulation result refers to a simulation value of the river channel into which the sediment flows after the parameters of the river basin model are calibrated.
The calculation of the silt reduction rate of each BMP scene is carried out by making a difference value between the model simulation value of the scene and a reference scene, and the specific calculation formula is as follows:
wherein, Fsed(X) represents the silt reduction rate (%) after BMP is applied, V (0) is the simulated amount (kg) of silt generated by erosion in the reference scenario, and V (X) is the simulated amount (kg) of silt generated by erosion in a certain BMP scenario.
The cost calculation of the BMP scenario uses the following formula:
wherein, Fcost(X) is the cost (dollar) of the BMP scenario, C (X)i) Represents the BMP implementation cost per unit area (Yuan/ha) on the ith plot; if no BMP is implemented on the plot, the cost is 0; if the BMP is implemented, calculating the cost per unit area of the BMP; a. theiIndicates the area of the ith plot. BMP expense data is obtained by field investigation or by looking up relevant documents; the cost of BMP used in this example is shown in table 2:
TABLE 2
(5) And (3) sequencing all individuals in the population by adopting a rapid non-dominant sequencing method according to the individual fitness calculation result, and regulating the non-dominant solution obtained by sequencing by utilizing an epsilon-dominant regulating strategy and reserving the solution in a set (called an elite set).
(6) Judging whether a termination condition (such as maximum evolution algebra) of the algorithm is reached, if the termination condition is not reached, performing crossing and mutation operations on the population to generate a new population, and turning to the step 4; and if the termination condition is reached, stopping executing the algorithm, and finally obtaining the Pareto optimal set or approximate optimal set as the elite set.
The crossing strategy of the genetic algorithm population adopts a tree-shaped coding crossing strategy, the crossing operator of the algorithm during genetic operation adopts a strategy of randomly selecting a certain tree node, then the tree nodes corresponding to the parents are crossed, and the tree structure is kept unchanged, as shown in fig. 4. After randomly selecting tree nodes, the crossover operator firstly judges whether the BMP codes of subtrees of the parent 1 and the parent 2 taking the nodes as roots are the same, and if the BMP codes are different, the two subtrees are exchanged; if the two codes are the same, the tree nodes are reselected, and then whether the BMP codes of the subtrees taking the node as the root of the parent are the same or not is compared, because when the codes of the two subtrees are the same, the exchange is not needed. And the mutation operator determines whether the individuals in the population are mutated according to the mutation probability given by the user and the random number (the range is 0-1), if the random number is less than the mutation probability given by the user, the individuals do not execute mutation operation, otherwise, the mutation operation is executed, namely, a random selection method is adopted to select a certain node number of the tree, and a new BMP type is generated by a random method to replace the original BMP type of the node.
In the following, we compared the method of the present invention with the conventional method under the same conditions, and the experimental steps are shown in fig. 5. Since the comparative experiments mainly involve the population initialization process of the epsilon-NSGA-II algorithm, the population scale parameters may have an effect on the optimization results. In consideration of the above situation, three groups of comparative experiments with population sizes of 60, 100 and 200 are designed, and the population sizes of 60, 100 and 200 are all common population size parameter values in genetic algorithms. In three experiments designed, the number of plots was 79; the main parameters of ε -NSGA-II are set as follows: the maximum evolution generation is 100, the mutation probability is 0.1, and epsilon (grid size) is 0.075.
The results of BMP optimization for the methods of the invention and the conventional methods are shown in figure 6 when the population sizes are 60, 100, 200, respectively. The results show that the Pareto optimal solutions obtained by the method are better than those obtained by the traditional method, and the results show that the method can improve the quality of the solutions by carrying out BMP optimization in consideration of the topological relation of the space of the land, is beneficial to algorithm convergence, and can quickly find out a better solution.
The embodiments described above are presented to enable a person having ordinary skill in the art to make and use the invention. It will be readily apparent to those skilled in the art that various modifications to the above-described embodiments may be made, and the generic principles defined herein may be applied to other embodiments without the use of inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make improvements and modifications to the present invention based on the disclosure of the present invention within the protection scope of the present invention.
Claims (1)
1. A method for optimizing management measures of a drainage basin by considering a topological relation of a land parcel comprises the following steps:
(1) integrally dividing the drainage basin into a plurality of independently managed plots with upstream and downstream relations, and constructing a plot tree, wherein the specific process comprises the following steps:
1.1 establishing a whole flow chart of the drainage basin by utilizing the DEM;
1.2 correspondingly taking all grids in the flow graph as nodes to build a tree structure according to the flow direction relation;
1.3 according to the land utilization map, combining nodes with the same land utilization type and direct flow direction relation in a tree structure, wherein the combined tree structure is the land parcel tree, each node is a corresponding land parcel, and the land parcels have a single land utilization type and a definite upstream and downstream relation;
(2) initializing and generating a population with a certain quantity of scales, wherein the specific process is as follows:
2.1 selecting a proper BMP for the root node or randomly generating the BMP of the root node by taking the plot where the drainage basin outlet is positioned as the root node of the plot tree according to the land utilization type;
2.2 for any plot except the root node in the plot tree, if the BMP of the downstream plot flowing directly is taken as an unforeseen measure, randomly selecting one of the unforeseen measures and other concrete measures as the BMP of the plot; if the BMP of the downstream plot which is directly flowed to is a specific measure, the BMP of the plot is made to be a non-measure; determining BMP of each plot in the plot tree according to the determined BMP, and obtaining an individual;
2.3, executing the steps 2.1 and 2.2 for multiple times to generate a plurality of individuals so as to form a population, wherein each individual in the population is a set of potential river basin overall management scheme, namely, the population corresponds to a plot tree, and each plot determines a corresponding BMP through a spatial interaction rule;
(3) and sequencing the individuals in the population by using a rapid non-dominant sequencing method according to the sediment reduction rate and the cost, wherein the calculation expressions of the individual sediment reduction rate and the cost are as follows:
wherein: fsed(X) shows the overall silt reduction in the basin after the implementation of the individual BMPs in the individual X, Fcost(X) represents the total cost of the BMP implementation for each block of the individual X, V (0) is the silt analog quantity generated by the erosion of the whole watershed without any measures for each block, V (X) is the silt analog quantity generated by the erosion of the whole watershed under the implementation condition of the BMP implementation for each block of the individual X, and C (X)i) Denotes the BMP implementation cost per unit area of the ith plot in the individual X, AiRepresenting the area of the ith land parcel, and n is the total land parcel number;
and then pairwise pairing the individuals for cross variation according to the sequence to generate a next generation population, and the specific process is as follows: for any pair of individuals, arbitrarily selecting a land parcel a from one of the individuals A except for the root node, and simultaneously selecting a land parcel B from the other individual B, wherein the land parcel B and the land parcel a are in the same position in the respective individuals; a subtree taking the land parcel a as a root node in the individual A and a subtree taking the land parcel B as a root node in the individual B are exchanged with each other to obtain an individual A 'and an individual B', so that the crossing process is completed; for the new individuals obtained after crossing, randomly selecting a plurality of individuals from the new individuals, randomly selecting a plot from the selected new individuals, and randomly selecting a specific measure or taking no measures to replace the original BMP of the plot, thereby completing the variation process;
(4) and (4) continuously carrying out genetic evolution according to the step (3) until a set iteration termination condition is reached, finally obtaining a new generation of population as an elite set, and further selecting an individual from the elite set as an optimal management scheme of the whole basin according to an environmental target and budget for basin governance.
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