DETECTION AND LOCALIZATION OF WATER LEAKS IN WATER NETS BY MEANS OF A MONITORING SYSTEM, HYDRAULIC MODEL AND NEURONAL

NETWORKS

Jan Studzinski*, Izabela Rojek** *Polish Academy of Sciences, Systems Research Institute (IBS PAN)

Newelska 6, 01-447 Warsaw, Poland E-mail: studzins@ibspan.waw.pl

** Kazimierz Wielki University in Bydgoszcz Institute of Mechanics and Applied Computer Science

Chodkiewicza 30, 85-064 Bydgoszcz, Poland E-mail: izarojek@ukw.edu.pl

KEYWORDS: Municipal water networks, hydraulic models, neuronal networks, SCADA systems, water leaks detection and localization. ABSTRACT

In the paper a complex approach to detect and localize the water leaks in water networks is presented. To realize the approach a sophisticated monitoring system is to design and implement on the water net, a water net hydraulic model has to be calibrated and afterwards neuronal nets to develop a water leaks classifier are used. The appropriate programs realizing these task are included into an ICT system developed at the Systems Research Institute of Polish Academy of Sciences. The neuronal nets used are of MLP and Kohonen types. INTRODUCTION

The failures that are arising in the municipal water networks cause commonly big water and money losses that results in essential growing of operational costs of the whole enterprise. The water losses can amount in some waterworks with an old infrastructure to 30% of the total water production what has an negative impact on their functioning (Saegrov, 2005). Because of that many publications in specialist journals are dealing with the approaches that could help to avoid the network failures and to minimize the water losses when they have been already arisen (Korbicz et al., 2004, Wyczolkowski and Moczulski, 2005, Wyczolkowski and Wysoglad, 2007, Studzinski, 2009, Studzinski and Rojek, 2012, Rojek, 2012 and 2013, Saegrow, 2005). In the latter case the algorithms to early detection and localization of the occurred failures and the subsequent water leaks are under development.

They can be constructed in three ways: firstly, with the use of only a SCADA system to monitor the work of the water net investigated and to signalize all untypical events, secondly, with using beside a SCADA also a hydraulic model of the water net to simulate the failure cases in the network and to find out the potential failure places by the comparison of untypical SCADA data with these simulated ones (Studzinski, 2009), and thirdly, with the use of a SCADA, hydraulic model and a neuronal net classifier that finds out the water net failure places automatically and more exact than it is made in the second approach (Wyczolkowski et al., 2005 and 2007, Studzinski and Rojek, 2012).

At the IBS PAN an integrated ICT system has been developed to complex management of communal water networks in

which the algorithms mentioned have been included (Stachura et al., 2012, Studzinski, 2012). The functions of the ICT system are more wide and they concern among others such the tasks as the water net optimization, planning and revitalization, pumps control in the pump stations installed on the network, calibration of the water net hydraulic model, planning of a SCADA system etc. But the algorithms to detect and localize the network failures are most interesting for the waterworks managers for they have indirect and fastest impact on the improvement of the water net operation. In the ICT system some modeling and optimization algorithms have been adopted that were developed by Straubel (Straubel and Holznagel, 1999) and used by him to support the management of the waterworks in Königs Wusterhausen near Berlin.

In (Studzinski and Rojek, 2012) an algorithm to detect and localize the water leaks in water nets by means of water networks has been presented and the conclusion was formulated that the tools of artificial intelligence fit good to solve such the practical tasks. In the following the results of subsequent investigation on using the neuronal nets to detect the water net failures are shown. In this case the structure of the neuronal nets used as the failure classifiers is different and more complicated than before and also another type of neuronal nets is tested.

CALCULATION APPROACH

In the case when the algorithm presented would be implemented into a waterworks the following approach steps must be conducted on the water network:

• Planning a SCADA system for the water net that will make possible the automatic calibration of the water net hydraulic model.

• Calibration of the hydraulic model using the SCADA system planed and implemented on the water net.

• Simulation of the water leaks on the water net by means of the hydraulic model calibrated.

• Recording in a data base the flow and pressure distributions in the measurement points of the SCADA system for the simulated water leaks.

• Development of a failures classifier using neuronal nets and the data concerning the flow and pressure distributions recorded.

• Designing the flow and pressure curves for standard operation of the water network for each SCADA measurements point.

When these steps are already completed then the subsequent actions are to made:

• On-line registration of the current pressure and flow values from the SCADA measurement points.

• When some measurement data are inadmissible different from these standard ones then putting into operation the neuronal classifier to find out the water net node or pipe with the presumable water leak.

In the algorithm presented the hydraulic model is a computer depiction of the real object while the neuronal classifier models the hydraulic model that is then treated as the object. In this way the water net modeling consists of two stages and the right calibration of the hydraulic model is in this approach of an essential meaning. On another side the rightness of the hydraulic model calibration depends on the density of the SCADA system installed on the water net and on the right localization of its measurement points (Farmani et al., 2007). In this way the two first steps of the approach presented, i.e. the SCADA planning and the hydraulic model calibration, are most important in the whole operation.

The idea of simulation of the water networks by means of neuronal nets and to use the neuronal models to conduct the fault diagnosis we have adopted from (Wyczolkowski et al., 2005 and 2007) and (Korbicz et al., 2004). The above algorithm has been tested on the real data obtained from the Polish waterworks in Rzeszow (Rojek, 2012 and 2013). DATA PREPARATION

All the calculations testing the algorithm have been done for an exemplary water network shown in Fig. 1. For computing the neuronal classifiers the MLP and Kohonen neuronal nets were used. The calculations were done for 20 and 10 monitoring points located on the water net and the simulations of water leaks were performed in 44 and in 37 nodes heuristically chosen on the network for the first and for the second case respectively.

Figure 1. The investigated water network.

The structure of the data file prepared for computing the neuronal nets is shown in Fig. 2. The last column of the file shows the numbers of the nodes with simulated water leaks and in the previous columns the flow values coming from the monitoring points are recorded. The first numerical record of the file shows the monitoring data registered for standard operation of the water net. The data got from the simulation runs have been multiplied and the prepared file consisted at last of 368 records. It was divided into teaching, testing and validation files in the proportions of 70%, 15% and 15% respectively.

Figure 2. Part of data file for teaching the neuronal nets.

While modeling the hydraulic model with the MLP nets consisting of three layers they were parameterized by two parameters: number of neurons on the hidden layer changing from 5 to 30 and number of teaching runs (epochs) taking the values 200 or 500 or 1000. In case of the Kohonen nets their parameterization was made by changing the number of neurons on the topological layer taking the values 2x8, 5x5 and 10x10 and by changing the number of teaching runs from 1000 to 20 000.

While calculating the MLP nets the transition (activation) functions used for the neurons on the hidden layer are optionally hyperbolic tangent, linear, logistic and exponential and the functions used for the neuron on the output layer are hyperbolic tangent, linear or softmax where the softmax function is:

The error functions used at the teaching runs are optionally the sum of squares function (SOS) and the cros entropy where the cross entropy function is:

with N - the number of examples used, yi - the calculated output value of the neuronal net and ti - the real output value from the data file.

By teaching the neuronal MLP nets the iterative BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithm is used to perform the optimization computing.

CALCULATION RESULTS

In Table 1 and in Fig. 3 the calculation results received while using the MLP nets for the creation of the water net failures classifier are shown.

Table 1. The MLP nets calculated for 20 monitoring points; Teaching, Testing and Validation qualities in %.

Figure 3. The qualities of the MLP nets obtained for 20 monitoring points in %.

As the best MLP classifiers the networks MLP 20-29-45, MLP 20-30-45 and MLP 20-22-45 have been appeared with their quality values of 100%, 100% and 98% respectively. The network quality depends on the number of neurons located on the hidden layer (the more neurons the better results) and the choice of the transition functions between the input and hidden layers and between the hidden and output layers. In these classifiers as the error function the cross entropy function was used and as the activation functions on the hidden and output layers the combinations of functions Tanh-Softmax or Logistic-Softmax appeared to be best. The other function combinations like Tanh-Logistic or Logistic-Linear or Exponential-Exponential were unsuccessful.

In Table 2 and in Fig. 4 the calculation results received while using the Kohonen nets for creating the classifier of the water net failures are shown. The quality of the Kohonen models depends above all on the number of neurons located on the topological layer with the similar rule as in the MLP case, i.e.

the more neurons the better results, but in general the results received for the Kohonen nets are worse than these of the MLP nets. As the best Kohonen classifier appeared the network SOFT 65-100 with 100 neurons on the topological layer (10x10) and with the simulation runs number of 1000. Its quality value equals to 75,51%.

The specific form of the Kohonen nets changes the number of model inputs, i.e. of neurons on the topological layer. To 21 columns resulted from 20 measurement points and from the column showing the number of the node with simulated water leak the number of all nodes in which the water leak was simulated (44 nodes for 20 monitoring points) is to add; ultimately 65 inputs result.

Table 2. Kohonen nets calculated for 20 monitoring points.

Figure 4. The errors of the Kohonen nets obtained for 20 monitoring points in %.

Table 3. The best MLP and Kohonen nets calculated for 20

monitoring points.

Figure 5. The network qualities for the best MLP and Kohonen nets calculated for 20 monitoring points in %.

The comparison between the MLP and Kohonen networks used as classifiers of the water net failures and calculated for 20 monitoring points one can show in Table 3 and in Fig 5.

As the best models have been appeared the following MLP nets: MLP 20-29-45 and MLP 20-30-45 for which the network quality equals to 100%.

Additionally also the calculations for the water net with only 10 monitoring installed on it have been conducted. In this case the water leaks have been simulated in 37 water net nodes. Because of that the number of output neurons in the MLP nets equals to 38 (37 neurons for the water leak nodes and 1 neuron for the standard operation of the water net) and the number of input neurons on the topological layer of the Kohonen nets equals to 48 (10 neurons for 10 monitoring points and 1 neuron for the standard operation of the water net without any water leak simulation and 37 neurons for the nodes with simulated water leaks). The results received for the best classifiers of both types of neuronal nets and for both cases of monitoring systems are shown in Table 4.

Table 4. The best MLP and Kohonen nets calculated for 10 and for 20 monitoring points.

One can see that in general the MLP classifiers are better than the Kohonen classifiers although the Kohonen nets are more complicated than the MLP ones. The second remark is that in case of MLP nets the number of monitoring points considered has got an essential impact on the detection and localization of the water leaks. The classifier MLP 10-23-38 is essentially worse than MLP 20-29-45 althogh the number of demaged water net nodes that it had to detect was smaller than in this another case. This observation does not concern the Kohonen nets that do not fit in general for solving the problems of finding out the water net failures with the use of neuronal nets.

ACKNOWLEDGEMENTS

The paper came into being and was sponsored by project no. POIG.01.03.01-14-034/12 of the Polish National Center of Research and Development whom the authors express their thankfulness. REFERENCES

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Dr. Izabela Rojek received the M.Sc. degree in computer science from Poznan University of Technology and the Ph.D. degree in mechanical engineering and operation from the same university in 2000. In 2001, she became an assistant professor at the Institute of Mechanical Technology, Poznan University of Technology, and then in 2002 at the Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University in Bydgoszcz. Her main research interest is in knowledge discovery, artificial intelligence and intelligent support systems.

Prof. Jan Studzinski, born in Warsaw, educated at the Technical University of Warsaw at the Faculty for Electrical Engineering and at the Warsaw University at the Faculty for Mathematics, working at the Systems Research Institute of Polish Academy of Sciences, where he has obtained his PhD. and Sc.D. degrees and where he leads the Center for Applications of Informatics in Environmental Engineering, author of 3 books and more than 250 scientific papers, dealing for many years with mathematical modeling and computer simulation of complex dynamical systems, with development of optimization and control methods and of computer aided decision support systems for management of communal waterworks, awarded for his work with several prices at the Belgian and International Trade Fairs for Technological Innovation held in Brussels, Board chairman of the Foundation for Development of System Sciences of Polish Academy of Sciences.