Skip to main content
Erschienen in: Water Resources Management 3/2018

13.11.2017

Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model

verfasst von: Masoud Karbasi

Erschienen in: Water Resources Management | Ausgabe 3/2018

Einloggen

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Evapotranspiration is one of the most important components in the optimization of water use in agriculture and water resources management. In recent years, artificial intelligence methods and wavelet based hybrid model have been used for forecasting of hydrological parameters. In present study the application of the Gaussian Process Regression (GPR) and Wavelet-GPR models to forecast multi step ahead daily (1–30 days ahead) reference evapotranspiration at the synoptic station of Zanjan (Iran) were investigated. For this purpose a 10-year statistical period (2000–2009) was considered, 7 years (2000–2006) for training and the final three years (2007–2009) for testing the various models. Various combinations of input data (various lag times) and different kinds of mother wavelets were evaluated. Results showed that, compared to the GPR model, the hybrid model Wavelet-GPR had greater ability and accuracy in forecasting of daily evapotranspiration. Moreover, the use of yearly lag times in the GPR and wavelet-GPR model increased its accuracy. Investigation of various kinds of mother wavelets also indicated that the Meyer wavelet was the most suitable mother wavelet for forecasting of daily reference evapotranspiration. The results showed that by increasing the forecasting time period from 1 to 30 days, the accuracy of the models is reduced (RMSE = 0.068 mm/day for one day ahead and RMSE = 0.816 mm/day for 30 days ahead). Application of the proposed model to summer season showed that the performance of the model at summer season is better than its performance throughout the year.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40CrossRef Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40CrossRef
Zurück zum Zitat Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 FAO. Rome 300:D05109 Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 FAO. Rome 300:D05109
Zurück zum Zitat Bishop CM (2006) Pattern recognition. Mach Learn 128:1–58 Bishop CM (2006) Pattern recognition. Mach Learn 128:1–58
Zurück zum Zitat Daubechies I (1992) Ten lectures on wavelets. SIAM Daubechies I (1992) Ten lectures on wavelets. SIAM
Zurück zum Zitat Karbasi M (2015) Forecasting of Daily Reference Crop Evapotranspiration Using Wavelet- Artificial Neural Network Hybrid Model (In Persian) Iranian. J Irrig Drain 9:761–772 Karbasi M (2015) Forecasting of Daily Reference Crop Evapotranspiration Using Wavelet- Artificial Neural Network Hybrid Model (In Persian) Iranian. J Irrig Drain 9:761–772
Zurück zum Zitat MacKay DJ (1998) Introduction to Gaussian processes NATO ASI Series F. Comp Syst Sci 168:133–166 MacKay DJ (1998) Introduction to Gaussian processes NATO ASI Series F. Comp Syst Sci 168:133–166
Zurück zum Zitat Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693CrossRef Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693CrossRef
Zurück zum Zitat Mallat S (1999) A wavelet tour of signal processing. Academic press Mallat S (1999) A wavelet tour of signal processing. Academic press
Zurück zum Zitat Meshram D, Gorantiwar S, Mittal H, Jain H (2015) Forecasting of Pomegranate (Punica granatum L.) evapotranspiration by using Seasonal ARIMA Model. Indian J Soil Conserv 43:38–46 Meshram D, Gorantiwar S, Mittal H, Jain H (2015) Forecasting of Pomegranate (Punica granatum L.) evapotranspiration by using Seasonal ARIMA Model. Indian J Soil Conserv 43:38–46
Zurück zum Zitat Partal T (2016) Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data KSCE. J Civ Eng 20:2050–2058 Partal T (2016) Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data KSCE. J Civ Eng 20:2050–2058
Zurück zum Zitat Patil AP, Deka PC (2017) Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India. Neural Comput & Applic 28:275–285CrossRef Patil AP, Deka PC (2017) Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India. Neural Comput & Applic 28:275–285CrossRef
Zurück zum Zitat Ramana RV, Krishna B, Kumar S, Pandey N (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711CrossRef Ramana RV, Krishna B, Kumar S, Pandey N (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711CrossRef
Zurück zum Zitat Rasmussen CE, Williams CK (2006) Gaussian processes for machine learning. 2006 The MIT Press, Cambridge, MA, USA 38:715-719 Rasmussen CE, Williams CK (2006) Gaussian processes for machine learning. 2006 The MIT Press, Cambridge, MA, USA 38:715-719
Zurück zum Zitat Wallen RD (2004) The illustrated wavelet transform handbook. Biomed Instrum Technol 38:298–298 Wallen RD (2004) The illustrated wavelet transform handbook. Biomed Instrum Technol 38:298–298
Zurück zum Zitat Williams CK (1997) Regression with Gaussian processes. In: Mathematics of Neural Networks. Springer, pp 378–382 Williams CK (1997) Regression with Gaussian processes. In: Mathematics of Neural Networks. Springer, pp 378–382
Metadaten
Titel
Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model
verfasst von
Masoud Karbasi
Publikationsdatum
13.11.2017
Verlag
Springer Netherlands
Erschienen in
Water Resources Management / Ausgabe 3/2018
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-017-1853-9

Weitere Artikel der Ausgabe 3/2018

Water Resources Management 3/2018 Zur Ausgabe