Maximum Rainfall Model Based on Archival Pluviographic Records – Case Study for Legnica (Poland)

Kaźmierczak, Bartosz; Wdowikowski, Marcin

doi:10.3311/ppci.8341

Cited by 18 publications

(33 citation statements)

References 41 publications

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“…Moreover, it is necessary to remember that assessing the failure frequency of water pipes should be carried out together with the estimation of proper operation of sewerage systems, e.g. storm water system [25,26]. These two municipal systems are very important for proper functioning of the whole buried infrastructure and should be considered together.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

Kutyłowska

2017

E3S Web Conf.

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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.

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Section: Resultsmentioning

confidence: 99%

Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

Kutyłowska

2017

E3S Web Conf.

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“…flood protection [15] and proper dimensioning of sewerage system [16]. This function (relation (1)) is also called a nonstationary availability indicator describing the sum of probabilities of occurrence of individual object operational event B i [10,11].…”

Section: Reliability and Failure Analysismentioning

confidence: 99%

Neural Network Approach for Availability Indicator Prediction

Kutyłowska

2017

Period. Polytech. Civil Eng.

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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.

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“…In the paper [13], an attempt has been made to develop two maximum precipitation models for Legnica, with the use of generalized exponential distribution (GED) -considered to be the best probability distribution for analyzed meteorological station [11]. The first model based on rainfall data with durations ranging from 5 minutes to 6 days, while the second model was developed on the basis of rainfall data of durations from 10 minutes to 6 days and the amounts of precipitation with the duration of 5 minutes were extrapolated.…”

Section: Introductionmentioning

confidence: 99%

“…Fréchet, gamma, log-normal and Weibull distributions [11], in order to analyze the possibility of using 10-minute rainfall series to determine the maximum rainfall amount with 5 minutes duration.The analysis was carried out using archival pluviographs from Legnica from the time span 1961-2010. Distribution parameters were calculated (for rainfall duration from 10 to 8640 minutes) and the model describing the dependence of the amount of rainfall on its duration and specified exceedance probability was subsequently established.…”

Section: Introductionmentioning

confidence: 99%

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The analysis of the possibility of using 10-minute rainfall series to determine the maximum rainfall amount with 5 minutes duration

et al. 2017

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Abstract. Modern scientific research in the area of heavy rainfall analysis regarding to the sewerage design indicates the need to develop and use probabilistic rain models. One of the issues that remains to be resolved is the length of the shortest amount of rain to be analyzed. It is commonly believed that the best time is 5 minutes, while the least rain duration measured by the national services is often 10 or even 15 minutes. Main aim of this paper is to present the difference between probabilistic rainfall models results given from rainfall time series including and excluding 5 minutes rainfall duration. Analysis were made for long-time period from 1961-2010 on polish meteorological station Legnica. To develop best fitted to measurement rainfall data probabilistic model 4 probabilistic distributions were used. Results clearly indicates that models including 5 minutes rainfall duration remains more appropriate to use.

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Maximum Rainfall Model Based on Archival Pluviographic Records – Case Study for Legnica (Poland)

Abstract: Abstract

Cited by 18 publications

References 41 publications

Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

Prediction of Availability Indicator of Water Pipes Using Artificial Intelligence

Neural Network Approach for Availability Indicator Prediction

The analysis of the possibility of using 10-minute rainfall series to determine the maximum rainfall amount with 5 minutes duration

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