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An Ensemble Neural Network Model to Forecast Drinking Water Consumption
https://orcid.org/0000-0002-3759-6421
https://orcid.org/0000-0002-2268-6600
https://orcid.org/0000-0003-4409-9463
A reliable short-term forecasting model is fundamental to managing a water distribution system properly. This study addresses the problem of the efficient development of a deep neural network model for short-term forecasting of water consumption in small-scale water supply systems. These aqueducts experience significant fluctuations in their consumption due to a small number of users, making them a challenging task. To deal with this issue, this study proposes a procedure to develop an ensemble neural network model. To reinforce the ensemble model to successfully deal with the weekly and yearly seasonality which affect these data, two different time-varying correction modules are proposed. To constitute the ensemble model, the simple recurrent neural network, the long short-term memory, the gated recurrent unit, and the feedforward architectures are analyzed in two case studies. The results show that the proposed ensemble model can achieve a robust and reliable prediction for all four of the architectures adopted. In addition, the results highlight that the proposed correction modules can significantly improve the predictions.
An Ensemble Neural Network Model to Forecast Drinking Water Consumption
https://orcid.org/0000-0002-3759-6421
https://orcid.org/0000-0002-2268-6600
https://orcid.org/0000-0003-4409-9463
A reliable short-term forecasting model is fundamental to managing a water distribution system properly. This study addresses the problem of the efficient development of a deep neural network model for short-term forecasting of water consumption in small-scale water supply systems. These aqueducts experience significant fluctuations in their consumption due to a small number of users, making them a challenging task. To deal with this issue, this study proposes a procedure to develop an ensemble neural network model. To reinforce the ensemble model to successfully deal with the weekly and yearly seasonality which affect these data, two different time-varying correction modules are proposed. To constitute the ensemble model, the simple recurrent neural network, the long short-term memory, the gated recurrent unit, and the feedforward architectures are analyzed in two case studies. The results show that the proposed ensemble model can achieve a robust and reliable prediction for all four of the architectures adopted. In addition, the results highlight that the proposed correction modules can significantly improve the predictions.
An Ensemble Neural Network Model to Forecast Drinking Water Consumption
https://orcid.org/0000-0002-3759-6421
https://orcid.org/0000-0002-2268-6600
https://orcid.org/0000-0003-4409-9463
A reliable short-term forecasting model is fundamental to managing a water distribution system properly. This study addresses the problem of the efficient development of a deep neural network model for short-term forecasting of water consumption in small-scale water supply systems. These aqueducts experience significant fluctuations in their consumption due to a small number of users, making them a challenging task. To deal with this issue, this study proposes a procedure to develop an ensemble neural network model. To reinforce the ensemble model to successfully deal with the weekly and yearly seasonality which affect these data, two different time-varying correction modules are proposed. To constitute the ensemble model, the simple recurrent neural network, the long short-term memory, the gated recurrent unit, and the feedforward architectures are analyzed in two case studies. The results show that the proposed ensemble model can achieve a robust and reliable prediction for all four of the architectures adopted. In addition, the results highlight that the proposed correction modules can significantly improve the predictions.
An Ensemble Neural Network Model to Forecast Drinking Water Consumption
J. Water Resour. Plann. Manage.
Zanfei, Ariele (author) / Menapace, Andrea (author) / Granata, Francesco (author) / Gargano, Rudy (author) / Frisinghelli, Matteo (author) / Righetti, Maurizio (author)
2022-05-01
Article (Journal)
Electronic Resource
English
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