CN112966221A - Method for converting total water consumption of assessment year in southern rich water region - Google Patents
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Abstract
The invention discloses a method for converting total water consumption of assessment years in southern rich water areas, which comprises the steps of collecting historical regional long series and annual assessment rainfall and water consumption data, determining a regional annual rainfall frequency curve, establishing a regional farmland irrigation mu average net water consumption and annual rainfall relation curve based on an artificial neural network model on the basis of calculating the historical long series and annual assessment farmland irrigation mu average net water consumption, further calculating regional farmland irrigation water consumption conversion coefficients under the condition of different annual rainfall frequencies, and obtaining the total regional assessment year converted water consumption. The method can accurately capture the nonlinear relation between the average net water consumption per mu and the annual precipitation of regional farmland irrigation, can consider the farmland irrigation water supply damage condition existing in the dry year exceeding the guarantee rate, better accords with the water resource utilization characteristics of southern rich water areas, and is beneficial to improving the scientificity and operability of the total water consumption conversion of the southern rich water areas in the assessment year.
Description
Technical Field
The invention belongs to the field of water resource management, and particularly relates to a method for converting total water consumption of southern rich water areas in assessment years.
Background
The assessment of the total amount of regional water is one of the main contents of the most strict water resource management system assessment, and has important significance for exerting the water resource rigidity constraint effect and promoting the sustainable development of the economic society. At present, a total water consumption control index for assessment is established under the condition of years of average water consumption (horizontal years), and reflects the restriction of the total water consumption under the condition of years of average water consumption. However, natural incoming water is highly variable, resulting in significant fluctuations in the total water usage of the region over the years of different incoming water frequencies.
At present, research results of a conversion method of total annual water consumption in regional assessment are few, and scholars propose a conversion assessment method of total annual water consumption based on a water consumption characteristic curve (namely a functional relation between the total regional water consumption and precipitation). However, this method has the following problems: (1) the situation of water supply damage in the dry year exceeding the guarantee rate is not considered, and the basic assumption is not in accordance with the actual situation; (2) the total water consumption series of the regions in the method is an event series which occurs in the same horizontal year, is a simulation calculation result, does not exist in reality, needs to depend on a more complex water resource configuration model, and influences the popularization and the use of the region with weaker part of foundation and lack of corresponding technical means.
For southern rich water areas, the water consumption of regional industry, life and the like is basically not influenced by the change of annual precipitation, and the fluctuation of the total amount of regional water along with the annual precipitation is mainly caused by the fluctuation of the water consumption of farmland irrigation along with the annual precipitation; in addition, the water quantity obtained from the water source by the direct-flow thermal power and nuclear power cooling system is discharged to the downstream of the water intake to return to the river channel only once through the heat exchange equipment, the water consumption is usually very low, the water quantity obtained from the water source cannot be completely calculated as the water consumption, and the corresponding water withdrawal quantity needs to be reduced by nuclear power. Therefore, the key points of the southern water-rich region in the conversion of the total annual water consumption are the water consumption for farmland irrigation and the water consumption for direct-current fire (nuclear) electric cooling. A conversion method for total water consumption in the assessment year, which can reflect the characteristics of the southern rich water region, is urgently needed to be researched and developed so as to improve the scientificity and operability of the assessment work of the total water consumption in the southern rich water region.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for converting total water consumption of southern rich water areas in the assessment year.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method for converting total water consumption of assessment years in southern rich water areas comprises the following steps:
step 1, collecting regional long history series and annual rainfall and water consumption data of examination;
step 2, selecting a proper frequency curve line type according to the regional historical long-series annual precipitation data in the step 1, estimating parameters of the frequency curve line type, and determining a regional annual precipitation frequency curve;
step 3, calculating the corresponding per mu average net water consumption of field irrigation according to the regional history long series and the assessment year water consumption data in the step 1;
step 4, establishing a relation curve between the regional farmland irrigation mu average net water consumption and the annual precipitation based on an artificial neural network model according to the regional historical long-series annual precipitation in the step 1 and the corresponding synchronous data of the farmland irrigation mu average net water consumption in the step 3;
step 5, calculating a conversion coefficient of the irrigation water consumption of the regional farmlands under the condition of different annual precipitation frequencies according to the regional annual precipitation frequency curve determined in the step 2 and the relationship curve of the average net water consumption per mu of the irrigation of the regional farmlands and the annual precipitation established in the step 4;
and 6, acquiring the total amount of the regional assessment year converted water according to the annual precipitation and water information of the regional assessment year in the step 1 and the conversion coefficient of regional farmland irrigation water consumption under the condition of different annual precipitation frequencies in the step 5.
In the step 2, the pearson type III distribution is used as a frequency curve line, and a linear moment method is used for estimating parameters of the frequency curve line.
In the step 4, the adopted artificial neural network model is a multilayer perceptron neural network, and a BP algorithm is adopted for learning and training.
In step 2, the Pearson III type distribution is used as a frequency curve line type, and a linear moment method is used for estimating parameters of the frequency curve line type.
In step 4, the adopted artificial neural network model is a multilayer perceptron neural network, and learning training is carried out by adopting a BP algorithm.
The method comprises the steps of collecting regional historical long series and assessment year rainfall and water consumption data, determining a regional year rainfall frequency curve, establishing a regional farmland irrigation mu average net water consumption and year rainfall relation curve based on an artificial neural network model on the basis of calculating the historical long series and assessment year farmland irrigation mu average net water consumption, further calculating regional farmland irrigation water consumption conversion coefficients under different year rainfall frequency conditions, and obtaining regional assessment year converted water consumption.
Compared with the prior art, the invention has the beneficial effects that:
the method can accurately capture the nonlinear relation between the average net water consumption per mu and the annual precipitation of regional farmland irrigation, can consider the farmland irrigation water supply damage condition existing in the dry year exceeding the guarantee rate, better accords with the water resource utilization characteristics of southern rich water areas, and is beneficial to improving the scientificity and operability of the total water consumption conversion of the southern rich water areas in the assessment year.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a topological structure of an artificial neural network model for regional farmland irrigation average acre net water consumption and annual precipitation.
FIG. 3 is a graph showing a relation curve between the average net water consumption per mu and the annual precipitation amount of regional farmland irrigation built on the basis of an artificial neural network model.
FIG. 4 is a graph of the conversion coefficient of regional field irrigation water usage under different annual precipitation frequency conditions.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings.
As shown in figures 1-4, a method for converting total water consumption of assessment years in southern rich water areas collects regional historical long series and assessment year rainfall and water consumption data, determines a regional year rainfall frequency curve, establishes a regional farmland irrigation mu average net water consumption and year rainfall relation curve based on an artificial neural network model on the basis of calculating the regional historical long series and assessment year farmland irrigation mu average net water consumption, and further calculates a regional farmland irrigation water consumption conversion coefficient under the condition of different year rainfall frequency rates to obtain the regional assessment year converted water consumption. Fig. 1 is a calculation flowchart of the present embodiment, which is performed according to the following steps:
1. collecting rainfall and water consumption data of long-history series and examination year.
The long-history series of annual precipitation data collected in the embodiment is annual precipitation P of M yearsi(i is 1,2, … M), and the long-series water use data of the history is N years of water consumption Q for irrigation of farmlandjAnd the effective utilization coefficient eta of irrigation water in farmlandj(j ≧ 1,2, … N), M.gtoreq.30 and M > N are typical.
The annual precipitation data collected in the examination in the embodiment is annual precipitation PcThe assessment year water consumption data is the actual total water consumption WTotal, cActual water consumption W for farmland irrigationAgricultural, cAnd water quantity W for direct-current fire (nuclear) electric coolingCold, c。
The unit of the annual precipitation in the concrete implementation is mm, and the unit of the actual total water consumption, the actual farmland irrigation water consumption and the direct-current fire (nuclear) electricity cooling water consumption is hundred million m3The unit of the average water consumption per mu for field irrigation is m3The effective utilization coefficient of the farmland irrigation water is dimensionless quantity.
2. And determining a regional annual precipitation frequency curve.
Selecting a proper frequency curve line type according to the regional historical long-series annual precipitation data in the step 1, estimating parameters of the frequency curve line type, and determining a regional annual precipitation frequency curve, wherein the step comprises two substeps:
2.1 frequency Curve form
Since the line shape of the annual precipitation population frequency curve is unknown, a line shape that fits well to most sample data sets is usually chosen.
The Pearson type III distribution is used as the frequency curve profile in this embodiment.
2.2 estimating parameters of the frequency profile
The conventional methods for estimating linear parameters of frequency curves mainly include a moment method, a probability weight moment method, a weight function method, a linear moment method, and the like. The linear moment method is a widely accepted and effective parameter estimation method at home and abroad at present, is less sensitive to the maximum value and the minimum value of a sequence than the conventional moment, and has more reliable estimated parameter values.
Annual precipitation P in this embodiment based on the long series of regional histories (M years) collected in step 1i(i ═ 1,2, … M) data, parameters of the frequency curve were estimated using a linear moment method.
3. Calculating the average net water consumption per mu in the irrigation of the farmland of the long history series and the check years.
And (3) calculating the corresponding average net water consumption per mu of the field irrigation according to the field irrigation water consumption per mu and the effective utilization coefficient data of the field irrigation water of the long regional history series (N years) and the assessment years collected in the step (1).
The average net water consumption per mu of field irrigation corresponding to the jth year in the historical series is calculated by the following formula:
Ej＝Qj·ηj(j＝1,2,…N) (1)
4. and establishing a relation curve between the average net water consumption per mu and the annual precipitation of regional farmland irrigation on the basis of an artificial neural network model.
Establishing a relation curve between the average net water consumption per mu and the annual precipitation of regional farmland irrigation on the basis of an artificial neural network model according to the regional historical long-series annual precipitation in the step 1 and the corresponding synchronous (N years) data of the average net water consumption per mu of the regional farmland irrigation in the step 3, wherein the step comprises two substeps:
4.1 Artificial neural network model selection
The multilayer perceptron neural network is a multilayer feedforward network model with one-way propagation, has strong nonlinear mapping capability, is one of the most basic network models in the research and application of the neural network at present, is proved to be a general function approximation method, and can be used for fitting complex functions.
The artificial neural network model used in this embodiment is a multilayer perceptron neural network.
As shown in FIG. 2, a topological structure diagram of an artificial neural network model for regional farmland irrigation equal acre net water consumption and annual precipitation is given.
4.2 Artificial neural network model learning Algorithm
The currently commonly used multilayer perceptron neural network learning algorithm mainly comprises a BP algorithm, a conjugate gradient method, a quasi-Newton method and the like, wherein the BP algorithm has strict theoretical basis and stronger generalization capability, can be used for self-adaption and autonomous learning, and is the most widely applied learning algorithm.
In the specific implementation, learning and training of the neural network of the multilayer sensor are carried out by adopting a BP algorithm according to regional historical long-series annual precipitation in the step 1 and corresponding synchronous (N-year) data of the average net water consumption per mu for field irrigation in the step 3. Obtaining a calculation formula of the average net water consumption per mu of regional farmland irrigation:
wherein,
the method is characterized in that the average net water consumption per mu of farmland irrigation calculated based on the multilayer sensor neural network is P, the annual precipitation is P, and f (·) represents the functional relation between the average net water consumption per mu of regional farmland irrigation and the annual precipitation established by the multilayer sensor neural network.
As shown in FIG. 3, a graph of the relationship between the average net water usage per acre and the annual precipitation for regional farmland irrigation built based on the artificial neural network model is shown. Wherein, the solid dots are data points of synchronization (N years) of regional historical long-series annual precipitation in the step 1 and corresponding farmland irrigation mu average net water consumption in the step 3, and the solid lines are relation curves of regional farmland irrigation mu average net water consumption and annual precipitation.
5. And calculating the conversion coefficient of the regional farmland irrigation water consumption under the condition of precipitation frequency in different years.
For any given annual precipitation frequency u (0 < u < 100%), the regional annual precipitation frequency curve determined in query step 2 can be used to obtain the corresponding annual precipitation PuAnd further on the basis of annual precipitation PuInquiring the relation curve between the average net water consumption per mu of regional farmland irrigation and the annual precipitation quantity established in the step 4, and calculating to obtain the corresponding average net water consumption per mu of farmland irrigation
Conversion coefficient K of regional farmland irrigation water consumption under condition of annual precipitation frequency uuCalculated by the following formula:
wherein,
and 4, inquiring the relation curve between the average net water consumption per mu of regional farmland irrigation and the annual precipitation quantity established in the step 4 to obtain the average net water consumption per mu of farmland irrigation corresponding to 50% of annual precipitation frequency (open water years).
As shown in FIG. 4, a curve of the conversion coefficient of regional field irrigation water under different annual precipitation frequency is shown.
6. And obtaining the total water consumption converted from the regional assessment year.
Obtaining the total amount of the regional assessment year reduced water according to the annual precipitation and water information of the regional assessment year in the step 1 and the conversion coefficient of the regional farmland irrigation water consumption under the condition of different annual precipitation frequencies in the step 5, wherein the step comprises three substeps:
6.1 calculating the reduction of the water consumption of farmland irrigation by regional assessment year
According to the annual precipitation of the assessment in the step 1cInquiring the regional annual precipitation frequency curve determined in the step 2 to obtain the corresponding annual precipitation frequency c, further inquiring the regional farmland irrigation water consumption conversion coefficient curve under the condition of different annual precipitation frequencies in the step 5 to obtain the regional farmland irrigation water consumption conversion coefficient K corresponding to the appraisal yearc。
Regional assessment year-based farmland irrigation water consumption
Calculated by the following formula:
6.2 calculation of direct-flow type water consumption reduction amount for cooling of nuclear (fire) electricity in regional assessment year
The water consumption for direct-current fire (nuclear) electric cooling is reduced into the water consumption for direct-current fire (nuclear) electric cooling in the regional assessment yearC is withdrawingCalculated by the following formula:
Wc is withdrawing＝WCold, c·(1-αc) (5)
Wherein, WCold, cWater consumption, alpha, for direct-flow nuclear (fire) cooling in regional assessmentcThe water consumption rate.
Water consumption rate alpha in this embodimentcThe yield was taken to be 5%.
6.3 calculating the total water consumption for the conversion of regional assessment years
Total water consumption for regional assessment year conversion
Calculated by the following formula:
substituting formulae (4) and (5) into formula (6) to obtain:
obtaining the total amount of water used for the regional assessment year
Then, can be
And total water consumption assessment control index WLAnd (3) comparison: when in use
When the water consumption of the examination year is qualified; when in use
And in time, the total water consumption of the assessment years is considered to be unqualified.
In conclusion, the regional annual precipitation frequency curve is determined by collecting regional historical long series and assessment annual precipitation and water consumption data, a regional farmland irrigation acre average net water consumption and annual precipitation relation curve is established based on an artificial neural network model on the basis of calculating the historical long series and assessment annual farmland irrigation acre average net water consumption, the regional farmland irrigation water consumption conversion coefficient under different annual precipitation frequency conditions is further calculated, and the regional assessment annual reduced water consumption total quantity is obtained. The method can accurately capture the nonlinear relation between the average net water consumption per mu and the annual precipitation of regional farmland irrigation, can consider the damage condition of farmland irrigation water supply existing in the low water years exceeding the guarantee rate, better accords with the water resource utilization characteristics of southern rich water areas, and is beneficial to improving the scientificity and operability of the total water consumption conversion of the southern rich water areas in the assessment year.
Claims (3)
1. A method for converting total water consumption of assessment years in southern rich water areas is characterized by comprising the following steps:
step 1, collecting regional long history series and annual rainfall and water consumption data of examination;
step 2, selecting a proper frequency curve line type according to the regional historical long series annual precipitation data in the step 1, estimating parameters of the frequency curve line type, and determining a regional annual precipitation frequency curve;
step 3, calculating the corresponding per mu average net water consumption of field irrigation according to the regional history long series and the assessment year water consumption data in the step 1;
step 4, establishing a relation curve between the regional farmland irrigation mu average net water consumption and the annual precipitation based on an artificial neural network model according to the regional historical long series annual precipitation in the step 1 and the corresponding synchronous data of the farmland irrigation mu average net water consumption in the step 3;
step 5, calculating a conversion coefficient of the regional farmland irrigation water consumption under different annual precipitation frequency conditions according to the regional annual precipitation frequency curve determined in the step 2 and the regional farmland irrigation mu average net water consumption and annual precipitation relation curve established in the step 4;
and 6, acquiring the total amount of the regional assessment year converted water according to the annual precipitation and water information of the regional assessment year in the step 1 and the conversion coefficient of regional farmland irrigation water consumption under the condition of different annual precipitation frequencies in the step 5.
2. The method of claim 1, wherein: in the step 2, the pearson type III distribution is used as a frequency curve line, and a linear moment method is used for estimating parameters of the frequency curve line.
3. The method of claim 1, wherein: in the step 4, the adopted artificial neural network model is a multilayer perceptron neural network, and a BP algorithm is adopted for learning and training.
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| CN115797101A (en) * | 2022-11-09 | 2023-03-14 | 江苏省水利科学研究院 | Method for converting historical available water supply amount under current engineering conditions of hilly and rocky areas in humid areas |
| CN118535932A (en) * | 2024-07-26 | 2024-08-23 | 江西省水利科学院(江西省大坝安全管理中心、江西省水资源管理中心) | Irrigation water consumption conversion method for matching precipitation with crop water demand |
| CN118535932B (en) * | 2024-07-26 | 2024-10-15 | 江西省水利科学院(江西省大坝安全管理中心、江西省水资源管理中心) | Irrigation water consumption conversion method for matching precipitation with crop water demand |
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