CN113343553B - Drought and flood water conservation and reception resource allocation method for supply and demand bilateral prediction - Google Patents
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Abstract
The invention discloses a drought and flood water conservation resource allocation method for supply and demand bilateral prediction, which belongs to the technical field of water resource optimal allocation. Comprising the following steps: collecting historical sequence information of water inflow and water inflow, and constructing a water supply and demand Bi-LSTM prediction model; predicting the water inflow and water demand of a region under a future preset scene, and predicting the drought and waterlogging state of a reservoir region in a future time period by combining the running state of the reservoir and the water consumption balance; constructing a multi-department multi-objective mixed integer dynamic optimization model for drought and waterlogging conservation; finally, the water resource optimal allocation scheme for the future drought and waterlogging condition is obtained. The invention can not only avoid drought and waterlogging disasters in the reservoir area to a great extent, but also obtain a water resource efficient allocation scheme for future drought and waterlogging and harvest, thereby providing a more reliable solution for sustainable water resource regulation and control and providing more robust guarantee for regional grain safety.
Description
Technical Field
The invention relates to the technical field of water resource optimal allocation, in particular to a drought and waterlogging water conservation resource allocation method for supply and demand bilateral prediction.
Background
With the rapid development of socioeconomic and the continuous advancement of the urbanization process, the contradiction between the increasing water resource demand and the water resource shortage is more prominent. In the global climate change context, the demand for future water resources will continue to increase. On the other hand, the water resource supply amount is changed by the comprehensive influence of a plurality of factors such as precipitation, air temperature, surface process and the like. The problem of water resources is expected to be a century challenge that is unavoidable in the future. Agriculture is a large household of water in China, and irrigation water is an important precondition for guaranteeing the safe production of grains. How to reasonably predict the supply and demand change of the future water resource, scientifically and efficiently allocate the limited water resource has profound significance for guaranteeing the grain safety and the regional sustainable development.
The optimization modeling method is widely applied in academia as an important tool for efficient configuration of water resources. Most of related researches are optimized for the current water resource utilization state, but the water resource optimization configuration for the future has more application prospect. Due to the complex conditions of hydrologic cycle, terrain variation, human activities and the like, future water resource supply and demand show nonlinear and random characteristics, and accurate prediction of the water resource supply and demand has great challenges. With the rapid development of machine learning algorithms, technical support is provided for reasonably processing sequence information and improving prediction accuracy. However, how to develop high-precision water supply and demand bilateral unified prediction by means of a machine learning algorithm, and to construct a prediction-optimization coupling frame, so as to realize efficient and reasonable configuration of water resources for the future, and no specific and effective implementation scheme exists.
On the other hand, in the climate change with global warming as a main background, the occurrence frequency of extreme climate hydrologic events such as drought, flood and the like is increased, and the occurrence frequency is inevitably one of important risks for sustainable development of affected areas. The reservoir can effectively relieve the impact of drought and waterlogging disasters on effective management of water resources by blocking and regulating natural runoffs so as to achieve the purposes of flood control, drought resistance and benefit, and furthest meet the requirements of regional development. The implementation of a scientific reservoir dispatching management scheme is helpful for exerting the engineering effect of the reservoir to the maximum extent. However, on the premise of considering the priority of the reservoir to bear tasks and ensuring engineering safety, how to fully utilize the regulation capacity of the reservoir, realize efficient management of water resources and consider the regional grain safety under drought and waterlogging conditions becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a drought and water conservation resource allocation method for bilateral prediction of supply and demand, which is characterized by comprising the following steps of:
step S1: collecting historical sequence information of water yield and water demand;
step S2: according to the historical sequence information of the step S1, a Bi-LSTM (Bi-directional Long Short-Term Memory network) prediction model for supplying and requiring water is used for respectively predicting the water inflow and the water inflow of the area under the future preset scene;
step S3: on the basis of predicting the water inflow and water demand of a future area, balancing the drought and waterlogging state of the predicted reservoir area in a future time period by combining the running state and the water consumption of the reservoir;
step S4: selecting the overall net economic benefit maximization of the area, the overall grain yield maximization of the area and the comprehensive water shortage index minimization as optimization targets, selecting the water balance capacity of a reservoir, the reservoir capacity limiting capacity of the reservoir, the flood storage capacity of a flood diversion area, the capacity expansion capacity of the flood diversion area, the grain safety and the water demand of each water department as constraint conditions, and constructing a multi-department multi-target mixed integer dynamic optimization model for drought and waterlogging and water conservation;
step S5: and (3) solving the multi-department multi-objective mixed integer dynamic optimization model in the step (S4) by using an analytic hierarchy process and a weighted minimum deviation process to obtain a water resource optimization configuration result under the condition of future drought and waterlogging.
Said step S4 comprises the sub-steps of:
step S41: the method comprises the steps of selecting an overall net economic benefit of a region as a target, considering water consumption of various departments of the region, diversion cost of a flood diversion region and capacity expansion cost of the flood storage region, and constructing an objective function:
wherein f 1 Is a net economic benefit; i is administrative area, and takes the values of 1,2, … and I; j is the value 1,2, …, J of the water department; t is a time period, and takes the values of 1,2, … and T; n is a flood diversion area, and the values are 1,2, … and N; c (C) ij Represents the j-th class water part of the i-th partitionNet benefit coefficient of the door; x is X ijt Representing the configuration water quantity of the water department of the jth class in the ith partition in the t period; d (D) n Representing the flood diversion cost of the nth flood diversion zone; w (W) tn Representing the diversion flow of the nth period of the nth flood diversion area; delta tn A variable of 0 to 1, wherein 1 represents that the flood diversion area needs to be expanded, and 0 represents that the flood diversion area does not expand; b (B) n Representing the expansion cost of the nth flood diversion area; e (E) tn Representing the capacity expansion capacity of the nth period of the nth flood diversion area;
step S42: maximizing the total yield of grains in a selected area, introducing secondary water production functions of different crops, and constructing an objective function:
wherein f 2 The total yield of the grains; k is the crop type, and the values are 1,2, … and K; a is that ik The planting area of the k-th crop in the ith partition; CW (continuous wave) tik The water quantity of the kth crop in the ith zone is configured in the t period; alpha, beta and o are coefficients of a crop secondary moisture production function obtained by fitting test data;
step S43: selecting a comprehensive water shortage index as a target, considering the configuration weights of all departments, and constructing an objective function:
wherein f 3 In order to integrate the index of water deficiency,the water distribution weight of the j water department of the i partition; />Maximum water demand for the ith partition and the jth water department in the t period;
step S44: the water balance capacity of the reservoir, the reservoir capacity limiting capacity of the reservoir, the flood storage capacity of the flood diversion area, the capacity expansion capacity of the flood diversion area, the grain safety and the water demand of each water utilization department are selected as constraint conditions, and the specific expression is as follows:
(1) Water balance constraint of reservoir:
in SR (repeat request) t 、SR t-1 The water storage capacity of the reservoir is respectively the t time period and the t-1 time period; WR (WR) t The water supply amount of the reservoir in the t period; when t=0, the water storage capacity of the reservoir is a known parameter, and when t is more than or equal to 1, the water inflow, water outflow and water storage capacity of the reservoir follow a dynamic balance principle;
(2) Reservoir capacity limiting constraint:
DR t ≤SR t ≤MR t
in the formula, DR t Reservoir dead volume for period t; MR (magnetic resonance) t The maximum water storage capacity of the reservoir is t time periods;
(3) Flood storage capacity constraint of flood diversion area:
W tn ≤WC tn
in WC tn Maximum flood storage capacity for t-period n flood diversion zone;
(4) Flood diversion area capacity restriction:
E tn ≤EC tn
in the formula, EC tn Maximum capacity expansion capacity of the flood diversion area in the period of t;
(5) Grain safety constraint:
wherein POP is the total population of the area; ND is the lowest grain standard of people;
(6) Water demand constraints for each water department:
in the method, in the process of the invention,minimum water demand for the jth water department in the t period for the ith partition; />Minimum water demand for the ith zone of the ith crop species in the t-th period; />Maximum water demand for the ith zone of the ith crop species in the t-th period;
x in the above constraint ijt 、CW tik And W is equal to tn Are all non-negative, i.e
X ijt ≥0,CW tik ≥0,W tn ≥0。
The invention has the beneficial effects that:
1. according to the invention, the randomness and complexity of a future water supply and demand structure are considered, the Bi-LSTM deep learning algorithm is adopted to develop the bilateral prediction of the water supply and demand of the water reservoir, so that the defects of the traditional prediction model for predicting the water supply and demand and the limitation of the dynamic influence of the bilateral prediction result of the supply and demand on the future water resource allocation in the aspect of prediction accuracy can be effectively overcome;
2. according to the invention, the future water resource supply and demand prediction information is fully utilized, and the modeling method is combined with the water resource optimal allocation, so that on one hand, the early warning of drought and waterlogging disasters in a reservoir area is realized, and on the other hand, the occurrence of disasters is avoided to a great extent through the efficient allocation of the water resources in the reservoir, and the regional grain safety under the conditions of water shortage and water enrichment is ensured.
Drawings
FIG. 1 is a flow chart of a method for configuring water resources for drought and flood conservation for double-side prediction of supply and demand.
Detailed Description
The invention provides a drought and flood water conservation resource allocation method for supply and demand bilateral prediction, and the invention is further described with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a method for configuring water resources for drought and flood conservation for double-side prediction of supply and demand. The method specifically comprises the following steps:
s1: collecting historical sequence information of water inflow and water inflow, preprocessing data, determining parameters of a Bi-LSTM model by taking part of the data as training data, constructing a water supply and demand Bi-LSTM prediction model, and taking the rest data as test verification model effects;
s2: respectively predicting the water inflow and the water inflow of the area under the future preset scene by using a water supply and demand Bi-LSTM prediction model;
s3: on the basis of predicting water inflow and water demand in a future area, predicting the drought and waterlogging state of a reservoir area in a future time period by combining the running state of the reservoir and water consumption balance;
s4: the method comprises the steps of constructing a multi-division multi-objective mixed integer dynamic optimization model for drought and waterlogging conservation by taking maximization of overall net economic benefit of a region, maximization of total grain yield and minimization of comprehensive water shortage index as optimization targets and taking reservoir energy regulation capacity, flood storage capacity of a flood diversion area, water demand of each division, grain safety and the like as constraint conditions;
s5: and solving the multi-objective optimization model by using an analytic hierarchy process and a weighted minimum deviation process to obtain a water resource optimization configuration result under the condition of future drought and waterlogging, and providing theoretical guidance and decision support for efficient management of regional water resources and grain security assurance.
The step S4 is divided into:
step S4-1: with the aim of maximizing the overall net economic benefit of the area, taking the water consumption of all departments of the area, the diversion cost of the flood diversion area and the capacity expansion cost of the flood storage area into consideration, constructing an objective function:
wherein f 1 Is to clear the channelEconomic benefit (yuan); i is administrative area; j is water department; t is the time period; n is a flood diversion area; c (C) ij Indicating the net benefit coefficient (meta/m 3 )；X ijt Represents the water quantity (m 3 )；D n Represents the flood cost (yuan/m) of the nth flood diversion zone 3 )；W tn Represents the diversion flow (m) of the nth flood diversion zone in the nth period 3 )；δ tn A variable of 0 to 1, wherein 1 represents that the flood diversion area needs to be expanded, and 0 represents that the flood diversion area does not expand; b (B) n Representing the capacity expansion cost (yuan/m) of the nth flood diversion area 3 )E tn Represents capacity expansion capacity (m) of nth period of nth flood diversion zone 3 )；
Step S4-2: with the aim of maximizing the total yield of regional grains, introducing secondary water production functions of different crops, and constructing target functions:
wherein f 2 Is grain yield (kg); k is the crop species; a is that ik The planting area (ha) of the kth crop in the ith zone; CW (continuous wave) tik For the ith zone and the kth crop in the t period (m 3 ) The method comprises the steps of carrying out a first treatment on the surface of the Alpha, beta and omicron are coefficients of a crop secondary water production function and are obtained by fitting test data;
step S4-3: taking comprehensive water shortage index minimization as a target, taking configuration weights of all departments into consideration, and constructing an objective function:
in the method, in the process of the invention,the water distribution weight of the j water department of the i partition can be calculated and obtained by an entropy weight method; />Maximum demand for the ith partition and jth water department in the t periodWater quantity (m) 3 )；
Step S4-4: the energy-regulating capacity of the reservoir, the flood-accumulating capacity of the flood-accumulating area, the water demand of each department, the grain safety and the like are taken as constraint conditions, and specific constraint expressions are expressed as follows:
water balance constraint of reservoir:
in SR (repeat request) t Is the water storage capacity (m) 3 )；WR t The water supply amount (m) of the reservoir in the t-th period 3 )；
In the constraint condition, when t=0, the water storage capacity of the reservoir is a known parameter, and when t is more than or equal to 1, the water inflow, the water outflow and the water storage capacity of the reservoir follow the dynamic balance principle.
Reservoir capacity limiting constraint:
DR t ≤SR t ≤MR t
in the formula, DR t Reservoir dead volume (m) for period t 3 )；MR t Is the maximum water storage capacity (m) 3 )；
Flood storage capacity constraint of flood diversion area:
W tn ≤WC tn
in WC tn Maximum flood capacity (m) for t-period n flood diversion zone 3 )；
Flood diversion area capacity restriction:
E tn ≤EC tn
in the formula, EC tn Maximum capacity (m) for t-period n flood diversion zone 3 )；
Grain safety constraint:
wherein POP is the total population (people) of the area; ND is the lowest food standard (kg/person);
water demand constraints for each water department:
in the method, in the process of the invention,for the ith partition and the jth water department, the minimum water demand (m 3 )；
Is the minimum water demand (m 3 )；
Is the maximum water demand (m 3 )；
0-1 variable constraint:
non-negative constraint:
X ijt ≥0,CW tik ≥0,W tn ≥0
the present invention is not limited to the preferred embodiments, and any changes or substitutions that would be apparent to one skilled in the art within the scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (1)
1. A drought and flood water conservation resource allocation method for bilateral prediction of supply and demand is characterized by comprising the following steps:
step S1: collecting historical sequence information of water yield and water demand;
step S2: according to the historical sequence information of the step S1, respectively predicting the water inflow and the water inflow of the area under the future preset scene by using a Bi-LSTM prediction model of the water supply and demand;
step S3: on the basis of predicting the water inflow and water demand of a future area, balancing the drought and waterlogging state of the predicted reservoir area in a future time period by combining the running state and the water consumption of the reservoir;
step S4: selecting the overall net economic benefit maximization of the area, the overall grain yield maximization of the area and the comprehensive water shortage index minimization as optimization targets, selecting the water balance capacity of a reservoir, the reservoir capacity limiting capacity of the reservoir, the flood storage capacity of a flood diversion area, the capacity expansion capacity of the flood diversion area, the grain safety and the water demand of each water department as constraint conditions, and constructing a multi-department multi-target mixed integer dynamic optimization model for drought and waterlogging and harvest;
said step S4 comprises the sub-steps of:
step S41: the method comprises the steps of selecting an overall net economic benefit of a region as a target, considering water consumption of various departments of the region, diversion cost of a flood diversion region and capacity expansion cost of the flood storage region, and constructing an objective function:
wherein f 1 Is a net economic benefit; i is administrative area, and takes the values of 1,2, … and I; j is the value 1,2, …, J of the water department; t is a time period, and takes the values of 1,2, … and T; n is a flood diversion area, and the values are 1,2, … and N; c (C) ij The net benefit coefficient of the j-th water department of the i-th partition is represented; x is X ijt Representing the configuration water quantity of the water department of the jth class in the ith partition in the t period; d (D) n Representing the flood diversion cost of the nth flood diversion zone; w (W) tn Representing the diversion flow of the nth period of the nth flood diversion area; delta tn Is a variable of 0-1, 1 represents a scoreThe flood area needs to be expanded, and 0 represents that the flood diversion area does not expand; b (B) n Representing the expansion cost of the nth flood diversion area; e (E) tn Representing capacity expansion capacity of an nth flood diversion area in an nth period;
step S42: maximizing the total yield of grains in a selected area, introducing secondary water production functions of different crops, and constructing an objective function:
wherein f 2 The total yield of the grains; k is the crop type, and the values are 1,2, … and K; a is that ik The planting area of the k-th crop in the ith partition is used as the planting area; CW (continuous wave) tik The water quantity of the kth crop in the ith zone is configured in the t period; alpha, beta and o are coefficients of a crop secondary moisture production function obtained by fitting test data;
step S43: selecting a comprehensive water shortage index as a target, considering the configuration weights of all departments, and constructing an objective function:
wherein f 3 In order to integrate the index of water deficiency,the water distribution weight of the j water department of the i partition; />Maximum water demand for the ith partition and the jth water department in the t period;
step S44: the water balance capacity of the reservoir, the reservoir capacity limiting capacity of the reservoir, the flood storage capacity of the flood diversion area, the capacity expansion capacity of the flood diversion area, the grain safety and the water demand of each water utilization department are selected as constraint conditions, and the specific expression is as follows:
(1) Water balance constraint of reservoir:
in SR (repeat request) t 、SR t-1 The water storage capacity of the reservoir is respectively the t time period and the t-1 time period; WR (WR) t The water supply amount of the reservoir in the t period; when t=0, the water storage capacity of the reservoir is a known parameter, and when t is more than or equal to 1, the water inflow, water outflow and water storage capacity of the reservoir follow a dynamic balance principle;
(2) Reservoir capacity limiting constraint:
DR t ≤SR t ≤MR t
in the formula, DR t Reservoir dead volume for period t; MR (magnetic resonance) t The maximum water storage capacity of the reservoir is t time periods;
(3) Flood storage capacity constraint of flood diversion area:
W tn ≤WC tn
in WC tn Maximum flood storage capacity for t-period n flood diversion zone;
(4) Flood diversion area capacity restriction:
E tn ≤EC tn
in the formula, EC tn Maximum capacity expansion capacity of the flood diversion area in the period of t;
(5) Grain safety constraint:
wherein POP is the total population of the area; ND is the lowest grain standard of people;
(6) Water demand constraints for each water department:
in the method, in the process of the invention,minimum water demand for the jth water department in the t period for the ith partition; />Minimum water demand for the ith zone of the ith crop species in the t-th period; />Maximum water demand for the ith zone of the ith crop species in the t-th period;
x in the above constraint ijt 、CW tik And W is equal to tn Are all non-negative, i.e
X ijt ≥0,CW tik ≥0,W tn ≥0；
Step S5: and (3) solving the multi-department multi-objective mixed integer dynamic optimization model in the step (S4) by using an analytic hierarchy process and a weighted minimum deviation process to obtain a water resource optimization configuration result under the condition of future drought and waterlogging.
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