A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks

2017

E3S Web Conf.

Abstract. The paper presents the results of artificial neural networks application to the availability indicator prediction. The forecasted results indicate that artificial networks may be used to model the reliability level of the water supply systems. The network was trained using 147 and 173 operational data from one Polish medium-sized city (distribution pipes and house connections, respectively). 50% of all data was chosen for learning, 25% for testing and 25% for validation. In prognosis phase, the best created network used 100% of 114 and 133 values for testing. Following functions were used to activate neurons in hidden and output layers: linear, logistic, hyperbolic tangent, exponential. The learning of the artificial network was performed using following input parameters: material, total length, diameter. In the optimal models hyperbolic tangent was chosen to activate the hidden and output neurons in modeling the availability indicator of house connections during 68 epochs of training. Hidden and output neurons were activated (20 epochs of learning) respectively by hyperbolic tangent and linear function during the prediction of availability indicator of distribution pipes. The maximum relative errors in learning and prognosis step were equal to 0.10% and 1.20% as well as 0.27% and 1.15% for distribution pipes and house connections, respectively.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

2017

E3S Web Conf.

“…The selected model was verified using data from the year 2014. Until now data describing a given water conduit, such as the material, the diameter, the age, the conduit length, the pressure, the type of soil and the conduit laying depth, have been used as input signals to model indicator λ [3,4,17]. In this research the input neurons were such parameters as: the number of house connections (NH), the length of water mains (L m ), the length of distribution pipes (L r ), the length of house connections (L p ), the number of failures of water mains (N m ), the number of failures of distribution pipes (N r ) and the number of failures of house connections (N p ).…”

Section: Investigative Methodologymentioning

confidence: 99%

“…That is the reason it seems to be reasonable to model also the failure frequency of water pipelines. Currently many studies dealing with the broadly understood frequency of failures of water conduits and with the risk of such failures are based on mathematical modelling using, e.g., fuzzy logic and artificial neural networks [17,18]. So far the failure intensity indicator for water conduits has been predicted using MLP ANNs.…”

Section: Frequency Of Failures Of Water Supply Networkmentioning

confidence: 99%

Comparison of Two Types of Artificial Neural Networks for Predicting Failure Frequency of Water Conduits

Period. Polytech. Civil Eng.

2016

This paper presents the results of a comparison between IntroductionArtificial neural networks (ANNs) are increasingly often used to model many engineering phenomena. According to literature reports, the multilayer perceptron (MLP) is currently one of the best ANN structures used to model various variables in broadly understood environmental engineering, e.g. to forecast hourly water demand time series [1], determine the pressure in a water supply system [2], predict the frequency of failures of a water distribution network [3, 4] and predict the dam water level [5]. So far there have been no reports on the application of radial basis functions (RBF) ANNs to predict the failure intensity (failure rate) indicator for water conduits. Therefore the principal aim of this research was to find out, using a simple example (basic input variables), if an artificial neural network of the RBF type would predict (with an error acceptable for engineering purposes) the failure frequency indicator for water conduits and whether this kind of ANN is competitive with a typical multilayer perceptron. Moreover, the motivation of comparison between RBF and MLP neural networks was to check the behaviour and results of radial basis function which have been also used by author in other regression methods, e.g. support vector machines. Such comparison could be useful in the future investigations in relation to choose the optimal algorithm of failure rate prediction.

“…As regards the prediction of reliability indicators and the general analysis of failure frequency, ANNs have been used mainly to determine the number of failures [30], predict the failure intensity indicator [31] and estimate pipeline construction costs [32]. The literature reports published so far indicate that the prediction of the availability indicator by means of ANNs is not extremely advanced, which encouraged the author to undertake this subject.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Neural Network Approach for Availability Indicator Prediction

Period. Polytech. Civil Eng.

2017

The principal aim of this research was to find out if artificial neu- IntroductionResearch on the frequency of failure of water supply networks has been conducted in Poland and in the world for many years [1][2][3][4]. Still, besides determining the failure frequency level of water conduits on the basis of operating data, it is also necessary to supplement such studies with the results of the modelling, using typical models or artificial intelligence and Monte Carlo simulations: probability of the failure occurrence and values of failure rate [5][6][7][8] or the amount of water losses through pipe breaks [9]. Therefore one of the aims of this research was to show that mathematical modelling could be used to determine the availability indicator and reliability level of water conduits.