Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling

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Küçüktopcu E., Cemek E., Cemek B., Simsek H.

SUSTAINABILITY, cilt.15, sa.7, 2023 (SCI-Expanded, SSCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 7
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/su15075689
  • Dergi Adı: SUSTAINABILITY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, Agricultural & Environmental Science Database, CAB Abstracts, Communication Abstracts, Food Science & Technology Abstracts, Geobase, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: Box-Jenkins, time series modeling, evapotranspiration, artificial intelligence, ARTIFICIAL NEURAL-NETWORK, ANN, PREDICTION, ET0
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Machine learning (ML) models, including artificial neural networks (ANN), generalized neural regression networks (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, this paper proposes hybridizations of ML and autoregressive integrated moving average (ARIMA) models to provide a more accurate and general forecasting model for evapotranspiration (ET0). The proposed models are developed and tested using daily ET0 data collected over 11 years (2010-2020) in the Samsun province of Turkiye. The results show that the ARIMA-GRNN model reduces the root mean square error by 48.38%, the ARIMA-ANFIS model by 8.56%, and the ARIMA-ANN model by 6.74% compared to the traditional ARIMA model. Consequently, the integration of ML with ARIMA models can offer more accurate and dependable prediction of daily ET0, which can be beneficial for many branches such as agriculture and water management that require dependable ET0 estimations.