Water demand forecasting using extreme learning machines

• Autorzy: Tiwari M. , Adamowski J. , Adamowski K.

Identyfikatory

DOI

10.1515/jwld-2016-0004

Warianty tytułu

PL

Przewidywanie zapotrzebowania na wodę z użyciem technik uczenia maszynowego

• Języki publikacji: EN

Abstrakty

EN

The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM_W, ELM_B), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN_W, ANN_B). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM_B and ANN_B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN_W and ELM_W models provided greater accuracy, with the ELM_W model outperforming the ANN_W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM_W model. The superiority of the ELM_W model over both the ANN_W or ANN_B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

PL

Oceniono zdolność modelowania z użyciem ekstremalnej maszyny uczącej się (ELM) stosowanej samodzielnie bądź w połączeniu z analizą falkową (W) lub metodami bootstrapowymi (B) (tzn. ELM, ELM_W, ELM_B) do przewidywania dobowego zapotrzebowania na wodę w mieście. Wyniki porównano z uzyskanymi tradycyjnymi metodami bazującymi na sztucznych sieciach neuronowych (tzn. ANN, ANN_W, ANN_B). Modele przewidujące zapotrzebowanie na wodę zbudowano z wykorzystaniem trzyletniego zapotrzebowania na wodę i zestawu danych klimatycznych dla miasta Calgary w kanadyjskiej prowincji Alberta. Hybrydowe modele ELM_B i ANN_B zapewniały satysfakcjonujące prognozy jednodniowe o podobnej dokładności, natomiast wyniki uzyskane z zastosowaniem modeli ELM_W i ANN_W były bardziej dokładne, przy czym model ELM_W okazał się lepszy niż ANN_W. Istotną poprawę prognozowania szczytowego zapotrzebowania na wodę w mieście uzyskano jedynie z zastosowaniem modelu ELM_W. Wyższość modelu ELM_W nad modelami ANN_W czy ANN_B dowodzi znaczącej roli transformacji falkowej w usprawnianiu działania modeli prognozujących zapotrzebowanie na wodę w mieście.

Słowa kluczowe

EN

artificial neural networks; bootstrap; Canada; extreme learning machines; uncertainty; water demand forecasting; wavelets

PL

bootstrap; ekstremalne maszyny uczące się; falki; Kanada; niepewność; prognozowanie zapotrzebowania na wodę; sztuczne sieci neuronowe

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2016
• Tom: no. 28
• Strony: 37--52
• Opis fizyczny: Bibliogr. 104 poz., rys., tab.

Twórcy

autor

Tiwari M.

• Anand Agricultural University, Department of Soil and Water Engineering, College of Agricultural and Technology, Gujarat, India

autor

Adamowski J.

• McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9

autor

Adamowski K.

• University of Ottawa, Department of Civil Engineering, Canada

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.