Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of daily failure rate using the serial triple diagram model and artificial neural network
In this study, 41 models used for the prediction of daily failure rates in water distribution networks have been designed via the Serial Triple Diagram Model (STDM) and artificial neural network (ANN) methods. For this purpose, daily failure data measured coordinately in the water distribution system network in Gebze and transferred to the geographic information system (GIS) has been used. The data has been normalized through the min-max technique to scale it at regular intervals and develop the model prediction performance. In this study, certain meteorological variables such as temperature and precipitation have been taken into account as model input for the first time. According to the increasing values of these two variables, it is observed in the model results that daily failure rates tend to increase. The expected model accuracies in failure rate prediction could not be obtained through the suggested ANN models. The higher prediction performances have been obtained through the STDM, a structure that enables visualization of the model results by making inferences. The STDM method is a significant alternative approach to determine the relationship of the variables on the failure and to predict the failure rate. It is predicted that the suggested STDM charts will contribute to the decision-makers, experts, and planners to determine effective infrastructure management. Also, investment planning prioritization will be able to reduce failure rates by interpreting prediction charts. HIGHLIGHTS Counter maps have been used for predicting and interpreting failure rates.; Temperature and precipitation effect has been directly taken into consideration for the first time.; The highest accuracy has been achieved with the least input in models.;
Prediction of daily failure rate using the serial triple diagram model and artificial neural network
In this study, 41 models used for the prediction of daily failure rates in water distribution networks have been designed via the Serial Triple Diagram Model (STDM) and artificial neural network (ANN) methods. For this purpose, daily failure data measured coordinately in the water distribution system network in Gebze and transferred to the geographic information system (GIS) has been used. The data has been normalized through the min-max technique to scale it at regular intervals and develop the model prediction performance. In this study, certain meteorological variables such as temperature and precipitation have been taken into account as model input for the first time. According to the increasing values of these two variables, it is observed in the model results that daily failure rates tend to increase. The expected model accuracies in failure rate prediction could not be obtained through the suggested ANN models. The higher prediction performances have been obtained through the STDM, a structure that enables visualization of the model results by making inferences. The STDM method is a significant alternative approach to determine the relationship of the variables on the failure and to predict the failure rate. It is predicted that the suggested STDM charts will contribute to the decision-makers, experts, and planners to determine effective infrastructure management. Also, investment planning prioritization will be able to reduce failure rates by interpreting prediction charts. HIGHLIGHTS Counter maps have been used for predicting and interpreting failure rates.; Temperature and precipitation effect has been directly taken into consideration for the first time.; The highest accuracy has been achieved with the least input in models.;
Prediction of daily failure rate using the serial triple diagram model and artificial neural network
Burak Kizilöz (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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