JP4745759B2 - River pollution load source estimation apparatus, method, and program - Google Patents

Description

  The present invention relates to a river pollution load source estimation technique, and more particularly to a river pollution load source estimation technique for estimating a generation source of a river pollution load at a predetermined river observation point.

  In recent years, an increase in environmental load on the natural water circulation system has become a problem. In river projects, it is important to manage and reduce water pollution in rivers in river environmental conservation projects. Therefore, it is necessary to accurately identify a river pollution negative source located upstream from the river observation point, that is, a river pollution source, and to efficiently take measures against the river pollution load.

River pollution load sources that affect river water quality are large in non-point (non-point source) pollution loads that accumulate daily in the upstream area of the river and point (point source) pollution loads such as treated water from sewage treatment plants. Separated.
Point pollution loads can be easily identified by measuring the amount of pollution load at the sewage treatment plant or other facilities, but the source of non-point pollution loads that flow directly into the river during rainfall without passing through such facilities. It is difficult to identify.

Conventionally, as a method of identifying river pollution load sources, many observation points have been established at river junctions, etc., and by comparing the amount of river pollution load measured at these observation points, the area that generates the pollution load can be identified. A method of narrowing down can be considered.
However, it is necessary to measure the river pollution load at many observation points in order to improve the accuracy of narrowing down to the extent that measures for river pollution load can be implemented as an environmental conservation project. For this reason, the equipment cost and the human cost burden required for measuring the river pollution load at such many observation points are drastically increased.

  On the other hand, river pollution load estimation technology that constructs a distributed pollution load runoff model that takes into account the effects of nonpoint pollution loads caused by rainwater using an arbitrary runoff analysis model has been proposed (for example, Patent Document 1). In such a river pollution load estimation technology, when estimating the river pollution load at any river observation point, it is assumed as a non-point load source in the rainfall area that affects the desired estimated river pollution load, that is, the rainfall impact range. All possible points are input, the process of water flow into the river is simulated, and the source of pollution load is estimated by balancing with the final output.

http://www.isc-wtg.co.jp/business/pdf/11.pdf, “Proposal of an efficient sewerage system using runoff analysis model”, International Water Consultant Co., Ltd.

However, in such a conventional technique, for example, a plurality of models such as a rainfall loss model, a surface runoff model, an in-pipe reasoning model, and a pollution load model are used in combination. There is a problem in that it requires a lot of time and expense. Moreover, since many estimations and assumptions are made in each model, there is a problem that sufficient estimation accuracy cannot be obtained.
The present invention is for solving such problems, and provides a river pollution load source estimation device, method, and program capable of obtaining sufficient estimation accuracy without requiring much time and expense. It is an object.

In order to achieve such an object, a river pollution load source estimation apparatus according to the present invention is a river pollution load source estimation apparatus that estimates a river water pollution load source, and the amount of precipitation in each district within the river basin A storage unit that stores precipitation data showing time series changes of rivers and river pollution load data showing time series changes of river pollution loads at any river observation point, and precipitation data of each region from the storage unit The river pollution load data is read, and the pattern coincidence with respect to the time series change with the river pollution load data is calculated for each precipitation data. And an arithmetic processing unit that outputs as a pollutant load source estimated distribution that affects the load.

At this time, a correlation value between precipitation data and river pollution load data may be used as the pattern matching degree.
The calculation processing unit may calculate the pattern coincidence by correcting the time difference required for rainwater to flow from the area to the river observation point, or the precipitation data and the river pollution load data. The time positions may be sequentially shifted to calculate the pattern matching degree, and the maximum value among these pattern matching degrees may be selected as the pattern matching degree of the area.

In addition, during the pattern matching analysis , the calculation processing unit calculates the rain pollution load from the difference between the river pollution load data and the non-rainy pollution load data showing the time series change of the river pollution load at the river observation point. The amount data may be calculated, and the obtained rain pollution load data may be used as river pollution load data.

In addition, the arithmetic processing unit calculates the pattern matching degree in each section as interpolation data by interpolating the pattern matching degree of each district, and using the obtained interpolation data, contour data indicating the distribution of pollution load sources is calculated. You may make it output.

A river pollution load source estimation method according to the present invention is a river pollution load source estimation method used in a river pollution load source estimation device that estimates a river water pollution load source, and a storage unit is provided in a river basin. A storage step for storing precipitation data indicating a time series change of precipitation in each district and a river pollution load data indicating a time series change of river pollution load at an arbitrary river observation point, and an arithmetic processing unit, A step of reading out precipitation data and river pollution load data of each area from the storage unit, calculating a pattern coincidence degree with respect to time series change with the river pollution load data for each precipitation data, and an arithmetic processing unit And outputting the obtained pattern matching degree of each area as a pollution load source estimated distribution that affects the river pollution load at the river observation point.

Moreover, the program concerning this invention is a program for functioning as each part which comprises any one river pollution load source estimation apparatus mentioned above.

According to the present invention, precipitation data indicating a time series change of precipitation in each district in the river basin and river pollution load data indicating a time series change of river pollution load at an arbitrary river observation point are respectively provided. Pattern matching analysis is performed, and the pattern matching degree of each area obtained by these analyzes is output as a pollution load source estimated distribution that affects the river pollution load at the river observation point. Compared with the case where the pollutant load is measured at each river junction, it is possible to significantly reduce the work burden and the cost burden.
Compared with the case of analyzing multiple models in combination, the data and work required to build the model are not required, reducing the time and cost burden, and the estimation accuracy is sufficient because the estimation and assumptions in these models are not passed. Is obtained.

Next, embodiments of the present invention will be described with reference to the drawings.
First, a river pollution load source estimation device according to an embodiment of the present invention will be described with reference to FIG. FIG. 1 is a block diagram showing a configuration of a river pollution load source estimation device according to an embodiment of the present invention.

  The river pollution load source estimation device 1 is composed of an information processing device such as a personal computer that outputs desired information by performing arithmetic processing on input processing information as a whole. It has a function to estimate the pollution load source that affects the river pollution load at the river observation point from the quantity and the river pollution load at the river observation point.

  Further, the river pollution load source estimation device 1 is connected to the meteorological data measurement system 2, the pollution load measuring device 3, or the pollution load estimation device 4 through the communication network 5, and precipitation data is obtained as necessary. And various data necessary for estimation of pollution load sources such as river pollution load data.

  In this embodiment, most of the non-point pollution loads distributed in river basins consist of sediments such as dust and dust accumulated in each area, and these sediments are carried by rainwater that falls in each area and flow into the river. Focusing on the fact that they are collected at river stations and measured as pollutant loads, the river pollutant load source estimator 1 uses precipitation data indicating time-series changes in precipitation in each area within the river basin and arbitrary Pattern matching analysis of river pollution load data showing time-series changes in river pollution load at each river observation point, and the pattern matching degree of each area obtained by these analyzes is converted into river pollution load at the river observation point. This is output as an estimated pollutant load source distribution.

The river pollution load used in the present embodiment is the total amount (per unit time) of COD (chemical oxygen demand), BOD (biochemical oxygen demand), or a similar pollution load index. Measured.
Also, in this embodiment, when it is not raining, it means that no rain has been observed over the entire rain affected area of the river station, and when it is raining, it is any of the rain affected areas of the river station. This refers to the state in which rainfall is observed and the time during which the rainfall is affected. The term “all-sky” means a state including non-rainy weather and rainy weather.

[Configuration of river pollution load source estimation device]
Next, with reference to FIG. 1, the structure of the river pollution load source estimation apparatus used with the river pollution load estimation system concerning one embodiment of this invention is demonstrated in detail.
The river pollution load source estimation device 1 is provided with an input unit 11, a screen display unit 12, an operation input unit 13, a storage unit 14, and an arithmetic processing unit 15 as functional units.

The input unit 11 includes a dedicated interface circuit unit, and has a function of fetching processing information and other data used for the pollutant load source estimation process from the communication network 5, an external device, or a recording medium, and stores the fetched data in the storage unit 14. It has a function.
Main processing information captured by the input unit 11 includes meteorological data including precipitation data, pollution load data, and non-rainy pollution load data.

The screen display unit 12 includes a screen display device such as an LCD or a PDP, and has a function of displaying various information such as an operation menu and a pollutant load source estimated distribution on the screen in response to an instruction from the arithmetic processing unit 15. .
The operation input unit 13 includes an operation input device such as a keyboard and a mouse, and has a function of detecting an operator operation and outputting the operation to the arithmetic processing unit 15.

The storage unit 14 includes a storage device such as a hard disk and a memory, and has a function of storing various processing information and a program 14P used for the pollution load source estimation process in the arithmetic processing unit 15. The program 14P is read in advance from an external device or a recording medium via an interface unit (not shown) and stored in the storage unit 14 in advance.
The main processing information stored in the storage unit 14 includes weather data 14A, river pollution load data 14B, non-rainy pollution load data 14C, rainy pollution load data 14D, pollution load source estimated distribution data 14E, and contour lines. There is data 14F.

  FIG. 2 is an explanatory diagram showing the relationship between river observation points and each district. In the present embodiment, the river observation point 21 provided in the river 20 to be estimated is set as a reference point, and the area 22 affected by the rainfall from the upstream side of the river observation point 21 is divided into meshes to provide each area 22. Yes.

The meteorological data 14A is time-series data indicating time-series changes in information on the weather environment such as precipitation, temperature, and sunshine measured in each area 22.
The river pollution load data 14 </ b> B is time-series data indicating time-series changes in the river pollution load measured at the river observation point 21.
The non-rainy pollution load data 14C is time-series data indicating a time-series change in the river pollution load measured at the river observation point 21 during non-rainy weather.

The rainy pollution load data 14D is time-series data indicating a time series change in the river pollution load at the time of rain at the river observation point 21, and the arithmetic processing unit 15 uses the river pollution load data 14B and the non-rainy pollution load. It is calculated from the difference with the data 14C.
The pollutant load source estimated distribution data 14E is data indicating the magnitude of the influence of each district 22 on the pollutant load at the river observation point 21. The calculation processing unit 15 causes the rain pollution load data 14C and each district 22 to be recorded. Calculated from the degree of pattern matching with precipitation data.
The contour line data 14F is data expressing the pollution load source estimated distribution data 14E with smooth contour lines, and is calculated by the arithmetic processing unit 15 by complementing the pollution load source estimated distribution data 14E.

The arithmetic processing unit 15 includes a microprocessor such as a CPU or DSP and its peripheral circuits, and has a function of realizing various functional units by reading and executing the program 14P from the storage unit 14.
As main functional means realized by the arithmetic processing unit 15, there are a rain pollution load calculation means 15A, a pattern matching analysis means 15B, and a contour line data calculation means 15C.

The rain / pollution load calculation means 15A has a function of calculating the rain / pollution load data 14D from the difference between the river pollution load data 14B and the non-rain pollution load data 14C.
The pattern matching analysis means 15B has a function for pattern matching analysis of precipitation data and rain pollution load data 14D included in the meteorological data 14A of each area 22, and pattern matching of each area obtained by the analysis. And the function of outputting the degree of contamination as estimated load distribution data 14E affecting the river pollution load at the river observation point 21.

  The contour line data calculating means calculates the pattern matching degree in the area between the districts 22 as interpolation data by performing an interpolation operation on the pattern matching degree of each district 22 stored as the pollution load source estimated distribution data 14E; It has the function of outputting contour line data 14F indicating the distribution of the pollutant load source using the obtained interpolation data.

[Operation of pollution load source estimation device]
Next, the operation of the pollution load source estimation device according to the embodiment of the present invention will be described with reference to FIG. FIG. 3 is a flowchart showing a pollution load source estimation process in the pollution load source estimation device according to the embodiment of the present invention.

The computation processing unit 15 of the pollution load source estimation device 1 starts the pollution load source estimation process of FIG. 3 by reading and executing the program 14P from the storage unit 14 in response to an operator instruction from the operation input unit 13. .
It is assumed that the storage unit 14 stores weather data 14A, river pollution load data 14B, and non-rainy pollution load data 14C in advance via the input unit 11 and stores them.

First, the arithmetic processing unit 15 reads the river pollution load amount data 14B and the non-rainy pollution load amount data 14C stored in the storage unit 14 by the rain / pollution load calculation unit 15A, and calculates the difference between them at each time. Then, the rain pollution load data 14D is calculated (step 100).
FIG. 4 is a graph showing river pollution load data. The river pollution load data 14B measured at the river observation point 21 includes not only the pollution load carried from the rainy area 22, but also the non-rainy pollution load carried from the rainy area 22, that is, non-rainage. Rainy pollution load data 14C is also included. On the other hand, the precipitation data shows only the rainfall in the district 22, as shown in FIG. Therefore, simply comparing the river pollution load data 14B and the precipitation data of each area 22 may cause an error, which may cause a decrease in the accuracy of the pattern match analysis.

  In order to reduce such an error, the rain / pollution load calculation means 15A obtains rain / pollution load data 14D obtained by subtracting the non-rain pollution load data 14C from the river pollution load data 14B as shown in FIG. ing. The rainy pollution load data 14D shows a value close to zero in the case of non-rainy weather (load) in the same manner as the precipitation data in FIG.

  Thereby, the error resulting from the river pollution load amount at the time of non-rainy weather included in the river pollution load data 14B can be reduced. In particular, the amount of river pollution load during non-rainy weather rises as the non-rainy period continues, as shown in FIG. This is presumably because dust and dust are likely to be generated due to drying by non-rainy weather. Therefore, in the case of the pattern matching analysis, the river pollution load data 14B and the precipitation data may be compared, but the pattern matching analysis is performed by using the rain pollution load data 14D instead of the river pollution load data 14B. The accuracy of can be increased.

  Since the non-rainy pollution load data 14C cannot be measured in the rainy period, for example, a value estimated by the pollution load estimation device 4 may be used. It should be noted that the average value of the river pollution load at the river observation point 21 during non-rainy weather and the river pollution load at the river observation point 21 immediately before the start of the rainy season (rainfall) are used as the non-rainy pollution load data 14C. Alternatively, a value interpolated from the river pollution load immediately before and after the start of the rainy period may be used as the non-rainy pollution load data 14C.

  Next, the arithmetic processing unit 15 uses the pattern matching analysis unit 15B to select, as the target district X, any district that has not been subjected to the pattern matching process among the districts 22 (step 101), and the precipitation amount in the target district X A pattern matching process is performed between the data W (X) and the rain / pollution load data P (14D), and the maximum pattern matching degree Cmax (X) is calculated as an index indicating the degree of matching (step 102). At this time, as a specific example of the pattern matching analysis, a general analysis method such as DP matching (Dynamic Programming) analysis may be used in addition to correlation analysis for obtaining a correlation value between the two as a pattern matching degree. .

  FIG. 7 is an explanatory diagram showing pattern matching processing. At the time of pattern matching, for example, precipitation data W (X) is sequentially shifted on the time axis to obtain the pattern matching degree C (X) of both, and the maximum pattern matching degree Cmax (X) is determined for both. Pattern coincidence degree is stored in the storage unit 14 as the pollutant load source estimated distribution data 14E.

  FIG. 8 is a configuration example of the pollutant load source estimated distribution data, and the maximum pattern coincidence calculated for each district is associated. This maximum pattern matching degree shows the similarity in the time-series change of the precipitation amount in the area and the pollution load at the river observation point 21, and the closer the maximum pattern matching degree is to zero, the more related ) Is weak and it is clear that the pollution load in the area is relatively small. It can also be seen that the closer the maximum pattern matching degree is to 1, the stronger the relationship (similarity) between the two, and the relatively large occurrence of pollution load in the area.

At this time, the time shift amount of precipitation data W (X) and rain pollution load data P when the maximum pattern matching degree Cmax (X) is obtained is the amount of rainwater flow from the target area X to the river observation point P. It corresponds to time. Accordingly, the rainwater flow time may be prepared in advance for each district, and the time difference between the two data may be corrected using the flow time of the corresponding district. Further, the time difference between the two data may be corrected based on the time difference obtained from the time difference between the peaks of both data, and the time difference between the two data can be corrected without preparing a time difference for each district.
In addition, as precipitation data W (X) and rain pollution load data P to be analyzed, matching analysis is performed with high accuracy by using river flow and precipitation data sequences measured over several years as patterns. Can do.

  In this way, the pattern matching analysis unit 15B determines whether or not the pattern matching process has been completed for all the districts (step 103). If there is an unprocessed district (step 103: YES), the process proceeds to step 101. Return and repeat the pattern matching process for the unprocessed district.

  On the other hand, when the pattern matching process is completed for all the districts (step 103: NO), the arithmetic processing unit 15 reads the pollutant load source estimated distribution data 14E stored in the storage unit 14 by the contour line data calculating unit 15C, The pattern coincidence is interpolated to generate the pattern coincidence at inter-district points as interpolation information (step 104), and the contour line data 14F indicating the pollution load source estimated distribution is calculated using the interpolation information. (Step 105), the contour line data 14F is displayed as a graph on the screen display unit 12 (Step 106), and a series of pollutant load source estimation processing is terminated.

  FIG. 9 is a screen display example (contour diagram) of unknown water generation distribution data. Here, the degree of pattern matching in each area is displayed by contour lines, and is displayed in different colors according to the intensity of the pollution load occurrence correlation. In this example, in particular, the region surrounded by the broken line has a higher pattern matching degree, indicating that more pollution load is generated. Note that many methods for calculating contour line data have been proposed, and known methods may be used.

  As described above, the present embodiment is based on the precipitation data showing the time series change of precipitation in each district in the river basin and the river pollution load showing the time series change of the river pollution load at any river observation point. The pattern matching analysis of each data was performed, and the pattern matching degree of each area obtained by these analyzes was output as the estimated pollution load source distribution that affects the river pollution load at the river observation point. Only one location at the river observation point is required, and the work burden and cost burden can be greatly reduced compared to the case where the pollution load is measured at each river junction.

  Compared with the case of analyzing multiple models in combination, the data and work required to build the model are not required, reducing the time and cost burden, and the estimation accuracy is sufficient because the estimation and assumptions in these models are not passed. Is obtained.

In addition, since the correlation value between precipitation data and river pollution load data is used as the pattern coincidence, it is possible to easily calculate an index indicating the similarity between the two without requiring complicated calculation processing. .
Also, during pattern matching, the time difference required for rainwater to flow from the area to the river observation point is corrected, and more specifically, the time positions of the precipitation data and the river pollution load data are sequentially shifted. Each pattern matching degree is calculated, and the maximum value of these pattern matching degrees is selected as the pattern matching degree of the area, so that the similarity between precipitation data and river pollution load data can be grasped with high accuracy. .

  In addition, the rain pollution load data is calculated from the difference between the river pollution load data and the non-rain pollution load data indicating the time series change of the river pollution load in the non-rainy weather at the river observation point. Since this rain pollution load data is used as river pollution load data, it is caused by the river pollution load during non-weather included in the river pollution load data actually measured at the river observation point. Errors in pattern matching analysis can be reduced.

  In addition, the pattern matching degree in each section is calculated as interpolation data by interpolating the pattern matching degree of each district, and contour data indicating the distribution of pollution load sources is output using the obtained interpolation data. Therefore, compared with the discrete distribution obtained from the pattern matching degree obtained for each district, the pattern matching degree in the area between the districts can be expressed continuously, and the position of the river pollution load source can be expressed. Easy to identify.

  In the above, although the evaporation of rainwater in the river and the route from each district to the river is not considered, the above evaporation amount is calculated based on the temperature data included in the weather data 14A of each district, Both precipitation data and pollution load data may be corrected. Thereby, the precision of pattern matching analysis can be improved.

It is a block diagram which shows the structure of the river pollution load source estimation apparatus concerning one embodiment of this invention. It is explanatory drawing which shows the relationship between a river observation point and each district. It is a flowchart which shows the pollution load source estimation process in the pollution load source estimation apparatus concerning one embodiment of this invention. It is a graph which shows non-rainy pollution load data. It is a graph which shows precipitation data. It is a graph which shows rainy pollution load data. It is explanatory drawing which shows a pattern matching process. It is a structural example of pollution load source estimation distribution data. It is an example of a screen display of contour line data.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... River pollution load source estimation apparatus, 11 ... Input part, 12 ... Screen display part, 13 ... Operation input part, 14 ... Memory | storage part, 14A ... Weather data, 14B ... Pollution load data, 14C ... Non-rainy pollution load Data, 14D ... Rainy pollution load data, 14E ... Pollution load source estimated distribution data, 14F ... Contour data, 14P ... Program, 15 ... Calculation processing unit, 15A ... Rainy pollution load calculation unit, 15B ... Pattern matching analysis unit, 15C: contour line data calculation means, 20 ... river, 21 ... river observation point, 22 ... district, 2 ... meteorological data measurement system, 3 ... pollution load measuring device, 4 ... pollution load estimation device, 5 ... communication network.

Claims (8)

A river pollution load source estimation device for estimating a river water pollution load source,
A storage unit for storing precipitation data indicating a time-series change in precipitation in each area within the river basin and river-pollution load data indicating a time-series change in river pollution load at an arbitrary river observation point ;
Reading the precipitation data and the river pollution load data of each district from the storage unit, and calculating the pattern matching degree regarding the time series change with the river pollution load data for each precipitation data, A river pollution load source estimation device, comprising: an arithmetic processing unit that outputs the obtained pattern matching degree of each district as a pollution load source estimation distribution that affects a river pollution load at the river observation point. In the river pollution load source estimation device according to claim 1,
The river pollution load source estimation device characterized in that the pattern matching degree includes a correlation value between the precipitation data and the river pollution load data. In claim 1,
The river pollution load source estimation device, wherein the arithmetic processing unit calculates the pattern coincidence by correcting a time difference required for rainwater to flow from the district to the river observation point. In claim 1,
The arithmetic processing unit sequentially shifts the time positions of the precipitation data and the river pollution load data to calculate pattern matching degrees, and selects the maximum value of the pattern matching degrees as the pattern matching degree of the district. River pollution load source estimation device characterized by that. In claim 1,
In the pattern matching analysis, the arithmetic processing unit determines whether the rainy weather pollution data is different from the difference between the river pollution load data and the non-rainy pollution load data indicating the time series change of the river pollution load in the non-rainy weather at the river observation point. A river pollution load source estimation device characterized by calculating pollution load data and using the obtained rain pollution load data as the river pollution load data. In claim 1,
The calculation processing unit calculates the pattern matching degree in each section as interpolation data by interpolating the pattern matching degree of each district, and uses the obtained interpolation data to indicate the distribution of the pollution load source river pollutant source estimation apparatus, wherein the benzalkonium to output the data. A river pollution load source estimation method used in a river pollution load source estimation device for estimating a river water pollution load source,
Storage unit stores a river pollutant load data indicating a time-series change of river pollution load in time series change any river observer and precipitation data indicating the precipitation amount in each section of the river basin storage Steps,
The arithmetic processing unit reads out the precipitation data and the river pollution load data of each district from the storage unit, and the pattern matching degree regarding the time series change with the river pollution load data for each precipitation data Calculating each of
The arithmetic processing unit, river pollution load source, characterized in that it comprises a step of outputting the pattern matching of the resulting said each district as pollutant source estimated distribution that affects river pollution load in the river observation point Estimation method. The program for functioning a computer as each part which comprises the river pollution load source estimation apparatus as described in any one of Claims 1-6.