CN110543719A - water supply pipeline leakage prediction method based on space metering model - Google Patents
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Abstract
the invention provides a water supply pipeline leakage prediction method based on a spatial metering model, and belongs to the technical field of water supply pipeline leakage. The method determines an index system influencing leakage of the water supply pipeline from pipeline properties, environmental factors and operation factors. Based on the established index system, EPANET software is utilized to simulate the index value of a water supply pipeline in a certain city in a certain day, the pipeline data of the water supply area A with the most serious leakage condition in the city are obtained, the pipeline data are divided into different spaces according to the serial number of the pipeline, the spatial correlation analysis is carried out on the water supply pipeline, a spatial weight matrix is defined, a spatial metering model is determined, a water supply pipeline leakage evaluation system is established, and the leakage condition of the pipeline is predicted.
Description
Technical Field
The invention relates to a water supply pipeline leakage prediction method based on a space metering model, and belongs to the technical field of water supply pipeline leakage.
Background
with the development of society, the urbanization process is continuously advanced, and the problem of leakage of water supply pipelines is becoming one of the important problems in water supply systems. For a long time, the pipeline leakage rate of urban water supply systems in China is high, so that the operation and development of water supply enterprises are hindered, and the urban water supply efficiency is seriously influenced. Therefore, how to explore the factors causing the leakage of the water supply pipeline, how to provide an effective method and a measure for reducing the leakage of the pipeline, how to accurately predict the leakage of the pipeline, how to accurately position the leakage point of the water supply pipeline, and how to reduce the leakage accidents of the pipeline are problems which need to be solved at present.
Disclosure of Invention
The invention provides a water supply pipeline leakage prediction method based on a space metering model in order to reduce the occurrence of pipeline leakage accidents, and the adopted technical scheme is as follows:
A water supply pipeline leakage prediction method based on a space metering model comprises the following steps:
Firstly, acquiring a prediction object, space, data and factors by combining EPANET software with a dynamic hydraulic model for analyzing the influence degree of the prediction object, the space, the data and the factors on pipeline leakage;
secondly, determining the spatial correlation existing between the pipelines through spatial correlation analysis;
thirdly, performing LM (Linear model analysis) inspection on pipeline leakage data of the water supply area A by using a jplv tool box in MATLAB (matrix laboratory) software to determine a spatial metering model;
fourthly, calculating each parameter estimation coefficient by using the model determined in the third step, and then explaining each factor by using each parameter estimation coefficient through a direct effect and an indirect effect to obtain estimation results of the direct effect and the indirect effect; and analyzing the influence of each factor on the pipeline leakage by using the estimation results of the direct effect and the indirect effect.
further, the first step of obtaining the predicted object, space, data and factors includes:
simulating data of 24 time periods in a certain day by using a water supply pipeline dynamic hydraulic model and taking one hour as a time node through EPANET software, and acquiring pipeline data of a water supply area A with the most serious leakage condition in the city as a prediction object;
Dividing the pipeline into a plurality of spaces according to different pipelines, wherein each pipeline is used as one space so as to complete the establishment, analysis and prediction of the model;
modeling the data of the first 20 time periods in the 24 time periods in the step one, researching the influence factors of the leakage rate of the water supply pipeline, and predicting and verifying the reliability of the model by using the data of the last 4 time periods;
and step four, selecting 7 factors of pipe diameter, pipe age, pipe length, soil covering thickness, pipeline running pressure and pipeline water flow speed as explanatory variables for analyzing the influence degree of the 7 factors on pipeline leakage.
Further, the specific number of the spaces in the second step is 39.
further, the second step of determining the spatial correlation existing between the pipes through the spatial correlation analysis includes:
step 1, establishing a spatial weight matrix by using a method based on the combination of the geographical position and the leakage rate, wherein the spatial weight matrix is as follows:
Wherein, the average value of the leakage rate of the first 20 time periods of the pipeline i and the pipeline j is respectively obtained;
Step 2, performing spatial autocorrelation analysis by using GeoDa software, and obtaining a Moran's I index value and a Moland scatter diagram of the leakage of each pipeline of the water supply area by using average value data of each index data of the water supply area A in the previous 20 time periods;
And 3, obtaining the spatial correlation existing between the pipelines according to the Moran's I index value and the Molan scatter diagram.
Further, the third step of the LM test determining the spatial metering model includes:
Step a: obtaining a random effect model of OLS regression by using an OLS regression method;
Step b: two LM (Lagrange multiplier test) judges LM-error and LM-lag;
Step c: if the judgment result is that: if both the LM-error test and the LM-lag test are not significant, keeping the OLS result; if the judgment result is that the two inspection results are both significant, executing the step d; if the judgment result is that only one is significant, selecting an SEM (Spatial Error Model, SEM) when the LM-Error is determined to be significant; when the LM-lag is judged to be obvious, an SAR model (space Auto regression model, SAR) is selected;
step d: further testing the RLM-lag and the RLM-error, and judging the test results of the RLM-lag and the RLM-error; if the judgment result is that the two inspection results are not significant, an OLS regression model is selected; if the judgment result is that the RLM-Error is obvious, selecting an SEM Model (Spatial Error Model, SEM); and if the judgment result is that the RLM-lag is obvious, selecting an SAR (synthetic Aperture Radar) model.
Wherein, LM-error, LM-lag, RLM-lag and RLM-error respectively represent 4 different statistics, namely variable space correlation test (LM-lag), error term space correlation test (LM-error), steady variable space correlation test (RLM-lag), and steady error term space correlation test (RLM-error).
the invention has the beneficial effects that:
The invention provides a method for predicting the leakage loss of the urban water supply network pipe sections based on a space metering model, explores the space distribution of the pipeline leakage loss of a water supply area A, establishes a space autoregressive model based on the physical topological connection of the pipe sections, analyzes the importance ranking and the leakage loss prediction of the urban water supply network pipe section level leakage loss, and provides a brand new strategy and technical means for the reconstruction of the urban water supply network pipe sections.
drawings
FIG. 1 is a flow chart of LM test according to the present invention;
FIG. 2 is a Moran scatter plot of the water supply area A of the present invention;
FIG. 3 is a comparison graph of predicted values and actual values at 20 to 21 in the method of the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples, but the present invention is not limited to these examples.
example 1:
A water supply pipeline leakage prediction method based on a space metering model comprises the following steps:
firstly, acquiring a prediction object, space, data and factors by combining EPANET software with a dynamic hydraulic model for analyzing the influence degree of the prediction object, the space, the data and the factors on pipeline leakage;
secondly, determining the spatial correlation existing between the pipelines through spatial correlation analysis;
thirdly, performing LM (Linear model analysis) inspection on pipeline leakage data of the water supply area A by using a jplv tool box in MATLAB (matrix laboratory) software to determine a spatial metering model;
Fourthly, calculating each parameter estimation coefficient by using the model determined in the third step, and then explaining each factor by using each parameter estimation coefficient through a direct effect and an indirect effect to obtain estimation results of the direct effect and the indirect effect; and analyzing the influence of each factor on the pipeline leakage by using the estimation results of the direct effect and the indirect effect.
Wherein, in the first step, the process of obtaining the prediction object, the space, the data and the factors comprises:
simulating data of 24 time periods in a certain day by using a water supply pipeline dynamic hydraulic model and taking one hour as a time node through EPANET software, and acquiring pipeline data of a water supply area A with the most serious leakage condition in the city as a prediction object;
Dividing the pipeline into a plurality of spaces according to different pipelines, wherein each pipeline is used as one space so as to complete the establishment, analysis and prediction of the model; wherein the specific number of spaces is 39;
modeling the data of the first 20 time periods in the 24 time periods in the step one, researching the influence factors of the leakage rate of the water supply pipeline, and predicting and verifying the reliability of the model by using the data of the last 4 time periods;
and step four, selecting 7 factors of pipe diameter, pipe age, pipe length, soil covering thickness, pipeline running pressure and pipeline water flow speed as explanatory variables for analyzing the influence degree of the 7 factors on pipeline leakage.
the data analyzed in this example are from water supply pipelines in M city in northern China. The city has established a dynamic hydraulics model for the urban water supply pipeline, and can realize the real-time simulation of the pipeline condition. Therefore, EPANET software is used for simulating 24 time periods of a certain day by taking one hour as a time node by utilizing a water supply pipeline dynamic hydraulic model, and acquiring pipeline data of a water supply area A with the most serious leakage condition in the city as a research object, wherein the pipeline data is divided into 39 spaces according to different pipelines, and each pipeline is used as one space so as to complete the establishment, analysis and prediction of the model. In the embodiment, the data of the first 20 time periods are adopted for modeling, influence factors of the leakage rate of the water supply pipeline are researched, and the data of the last 4 time periods are used for predicting and verifying the reliability of the model.
In the embodiment, 7 factors of pipe diameter, pipe age, pipe length, soil covering thickness, pipeline running pressure and pipeline water flow speed are selected as explanatory variables, and the influence degree of the factors on pipeline leakage is analyzed. Table 1 is a description of the selected correlation indices.
TABLE 1 pipeline leakage correlation index
since the variables include continuous variables and discrete variables, the variables need to be preprocessed for research convenience. For continuous variables, in order to eliminate bias errors generated by different dimensions on model estimation results, logarithmic processing needs to be carried out on the continuous variables; for discrete variables, it is necessary to convert them to 0-1 variables. The method is characterized in that the variables of the leakage rate, the pipe diameter, the pipe age, the pipe length, the covering soil thickness, the pipe pressure and the pipe water flow speed are logarithms, the variable pipe is converted into 0-1 variable, the variable pipe is divided into 4 variables, namely whether the variable pipe is a common cast iron pipe (isA), whether the variable pipe is a steel pipe (isB), whether the variable pipe is a PVC pipe (isC) and whether the variable pipe is a nodular cast iron pipe (isD), part of data after processing is shown in a table 2, and the meanings of letters in the table are shown in the table 1.
TABLE 2 partial data after pretreatment
the second step of determining the spatial correlation existing between the pipes through the spatial correlation analysis includes:
Step 1, establishing a spatial weight matrix by using a method based on the combination of the geographical position and the leakage rate, wherein the spatial weight matrix is as follows:
wherein, the average value of the leakage rate of the first 20 time periods of the pipeline i and the pipeline j is respectively obtained;
step 2, performing spatial autocorrelation analysis by using GeoDa software, and obtaining a Moran's I index value and a Moland scatter diagram of the leakage of each pipeline of the water supply area by using the average value data of each index data of the water supply area A in the previous 20 time periods, as shown in FIG. 2;
And 3, obtaining the spatial correlation existing between the pipelines according to the Moran's I index value and the Molan scatter diagram.
As shown in fig. 2, Moran's I has an index value of 0.468, and the corresponding p value is 0.001, and in the case of significance of 0.01, it can be considered that there is a significant positive spatial autocorrelation between the pipe leaks; the abscissa of the Moran scattergram is an explained variable, and the ordinate is a product of the explained variable and a space weight matrix, and is commonly used for researching space characteristics. The distribution of the tubes can be seen from the Moire plot shown in FIG. 2, with the tubes substantially distributed in the first and third quadrants, indicating that there is significant spatial correlation between the tubes and that it is positively influenced, i.e., there is the same varying leak rate between adjacent tubes. Further, the pipes adjacent to the pipe with high leakage rate also have higher leakage rate, and similarly, the pipe adjacent to the pipe with low leakage rate also has low leakage rate.
As shown in fig. 1, the third step of the LM test to determine the space metering model includes:
step a: obtaining a random effect model of OLS regression by using an OLS regression method; the specific form of the random effect model is as follows:
Wherein Rit is an explained variable, namely the leakage rate, rho is a space autoregressive coefficient, W is a space weight matrix, Dit, agenit, … and Vit are explanation variables, beta 1, beta 2 and … beta 11 are explanation variable estimation coefficients and reflect the influence degree of the explanation variables on the explained variables, alpha is a random effect model term, and epsilon it is a random error vector.
step b: two LM (Lagrange multiplier test) judges LM-error and LM-lag;
step c: if the judgment result is that: if both the LM-error test and the LM-lag test are not significant, keeping the OLS result; if the judgment result is that the two inspection results are both significant, executing the step d; if the judgment result is that only one is significant, selecting an SEM (Spatial Error Model, SEM) when the LM-Error is determined to be significant; when the LM-lag is judged to be obvious, an SAR model (space Auto regression model, SAR) is selected;
Step d: further testing the RLM-lag and the RLM-error, and judging the test results of the RLM-lag and the RLM-error; if the judgment result is that the two inspection results are not significant, an OLS regression model is selected; if the judgment result is that the RLM-Error is obvious, selecting an SEM Model (Spatial Error Model, SEM); and if the judgment result is that the RLM-lag is obvious, selecting an SAR (synthetic Aperture Radar) model.
wherein, LM-error, LM-lag, RLM-lag and RLM-error respectively represent 4 different statistics, namely variable space correlation test (LM-lag), error term space correlation test (LM-error), steady variable space correlation test (RLM-lag), and steady error term space correlation test (RLM-error).
The fourth step explains the concrete process of analysis as follows:
According to the model preliminary setting shown in the formula (1), the MATLAB software is used to calculate each parameter estimation coefficient, and each coefficient estimation value is shown in the second column of the table 3. For the spatial panel autoregressive model, due to the existence of the spatial effect, the estimation coefficients of the parameters of the model cannot directly represent the influence degree of each factor, and each factor needs to be explained through the direct effect and the indirect effect. The estimation results of the direct effect and the indirect effect are shown in table 3, and the influence of each factor on the pipeline leakage can be analyzed by estimating the direct effect and the indirect effect.
(1) Space factor: in the result of the spatial autoregressive model estimation, the spatial lag coefficient ρ is 0.408 and is significant at the level of 0.01, which means that the leakage of the pipeline is greatly affected by the leakage of the adjacent pipeline, and the leakage of the pipeline itself becomes more serious as the leakage of the adjacent pipeline becomes worse.
(2) the pipeline property: first, at a significant level of 0.1, the direct effects of three of the 0-1 variables of the pipe diameter and pipe (whether ordinary cast iron pipe, whether PVC pipe, and whether ductile cast iron pipe) were examined, and the coefficients of the three 0-1 variables of the pipe were all positive. Of the four 0-1 variables of the pipe, the coefficient is the largest whether the pipe is a nodular cast iron pipe or not, the value is 0.2636, the coefficient is the smallest whether the pipe is a steel pipe or not, the value is 0.0369, and the difference between the two values is 0.2267, which shows that if the pipe is replaced by the steel pipe from the nodular cast iron pipe, the leakage rate can be reduced by 0.2267 units; the pipe diameter passes significance test at a 0.1 level, the regression coefficient is negative, namely, the leakage loss is reduced along with the increase of the pipe diameter; the pipe length does not pass significance test, namely the influence of the pipe length on the pipeline leakage rate is not significant; the tube age also failed the significance test, probably because in the data used in the study in this chapter, the tube age data was not distributed uniformly, basically all 49 years, only 7 years and 29 years separately, so that the tube age was not significant to the pipeline leakage. In the estimation result of the indirect effect, only the pipe diameter passes the significance test of 0.1, which shows that the size of the pipe diameter can influence the leakage of the adjacent pipeline, and other factors in the pipeline property can not influence the adjacent pipeline.
(3) environmental factors: the coefficient of the thickness of the covering soil is negative and is remarkable at the level of 0.05, and the larger the thickness of the covering soil is, the smaller the influence of the ground bearing on the pipeline is, so that the leakage of the pipeline is smaller; and the direct effect and the indirect effect are obvious, which shows that the thickness of the covering soil can not only influence the leakage of the pipeline per se, but also influence the leakage of the adjacent pipeline. The leakage of the pipe is affected by the ground load of the pipe itself and the adjacent pipes.
(4) The operation factors are as follows: the running pressure and the water flow speed of the pipeline pass significance tests at the level of 0.05, the regression coefficients are 0.346 and 0.182 respectively, and the direct effect and the indirect effect are significant, which shows that the leakage of the pipeline can increase along with the increase of the running pressure and the water flow speed of the pipeline; excessive pressure and flow rate not only can cause leakage to the pipeline of the pipeline, but also can threaten the adjacent pipeline.
and (3) according to the determined equation (1), calculating a predicted value of the leakage rate of each pipeline of the water supply area A by using the pipe diameter, the pipe material, the pipeline running pressure, the pipeline water flow speed and the soil covering thickness value of the next 4 time periods, and checking the prediction effect. The following table only lists the prediction results of the time period from 20 to 21, the prediction relative error is in the range of [0.0051,0.0364], and the prediction effect is good.
according to the pipeline leakage prediction results in table 4, the leakage situation of each pipeline in the water supply area a can be found, the most serious leakage situation is the G01525 pipeline, and the pipeline is found to be located at the intersection of several water supply areas and is located in the urban water supply main line through searching. The least leakage is the G01428 pipe, which is in the branch line and the water supply pressure is low. Fig. 3 is a line graph of the predicted value and the true value of the time period, and it can be seen that the two line graphs almost coincide and the fitting effect is good.
TABLE 3 regression coefficients, direct effect and indirect effect estimation results
note: in the table, and indicate significance at 10%, 5% and 1% levels, respectively
TABLE 4 prediction of pipeline leakage
although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. a water supply pipeline leakage prediction method based on a space metering model is characterized by comprising the following steps:
Firstly, acquiring a prediction object, space, data and factors by combining EPANET software with a dynamic hydraulic model for analyzing the influence degree of the prediction object, the space, the data and the factors on pipeline leakage;
secondly, determining the spatial correlation existing between the pipelines through spatial correlation analysis;
thirdly, performing LM (loss measurement) inspection on the pipeline leakage data of the water supply area A to determine a space metering model;
fourthly, calculating each parameter estimation coefficient by using the model determined in the third step, and then explaining each factor by using each parameter estimation coefficient through a direct effect and an indirect effect to obtain estimation results of the direct effect and the indirect effect; and analyzing the influence of each factor on the pipeline leakage by using the estimation results of the direct effect and the indirect effect.
2. The method of claim 1, wherein said first step of obtaining predicted objects, spaces, data and factors comprises:
simulating data of 24 time periods in a certain day by using a water supply pipeline dynamic hydraulic model and taking one hour as a time node through EPANET software, and acquiring pipeline data of a water supply area A with the most serious leakage condition in the city as a prediction object;
Dividing the pipeline into a plurality of spaces according to different pipelines, wherein each pipeline is used as one space so as to complete the establishment, analysis and prediction of the model;
modeling the data of the first 20 time periods in the 24 time periods in the step one, researching the influence factors of the leakage rate of the water supply pipeline, and predicting and verifying the reliability of the model by using the data of the last 4 time periods;
And step four, selecting 7 factors of pipe diameter, pipe age, pipe length, soil covering thickness, pipeline running pressure and pipeline water flow speed as explanatory variables for analyzing the influence degree of the 7 factors on pipeline leakage.
3. The method for predicting water pipe leakage according to claim 2, wherein the specific number of spaces in step two is 39.
4. The method for predicting water pipe leakage according to claim 2, wherein the second step of determining the spatial correlation existing between the pipes through the spatial correlation analysis comprises:
Step 1, establishing a spatial weight matrix by using a method based on the combination of the geographical position and the leakage rate, wherein the spatial weight matrix is as follows:
Wherein, the average value of the leakage rate of the first 20 time periods of the pipeline i and the pipeline j is respectively obtained;
Step 2, performing spatial autocorrelation analysis by using GeoDa software, and obtaining a Moran's I index value and a Moland scatter diagram of the leakage of each pipeline of the water supply area by using average value data of each index data of the water supply area A in the previous 20 time periods;
And 3, obtaining the spatial correlation existing between the pipelines according to the Moran's I index value and the Molan scatter diagram.
5. the method of predicting water pipe leak as set forth in claim 1, wherein said third step of said LM testing the process of determining the spatial metric model comprises:
step a: obtaining a random effect model of OLS regression by using an OLS regression method;
step b: judging LM-error and LM-lag by the two LMs;
step c: if the judgment result is that: if both the LM-error test and the LM-lag test are not significant, keeping the OLS result; if the judgment result is that the two inspection results are both significant, executing the step d; if the judgment result is that only one is significant, selecting an SEM model when the LM-error is significant; when the LM-lag is judged to be obvious, an SAR model is selected;
Step d: further testing the RLM-lag and the RLM-error, and judging the test results of the RLM-lag and the RLM-error; if the judgment result is that the two inspection results are not significant, an OLS regression model is selected; if the judgment result is that the RLM-error is obvious, selecting an SEM model; and if the judgment result is that the RLM-lag is obvious, selecting an SAR model.
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