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Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs)

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  • Falamarzi, Yashar
  • Palizdan, Narges
  • Huang, Yuk Feng
  • Lee, Teang Shui

Abstract

Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate forecasting is essential in all water resources applications. In this study, artificial neural network (ANN) and wavelet neural network (WNN) were utilized to forecast daily ET from temperature and wind speed data. The WNN model used in this study is a neural network model with one hidden layer and a wavelet function as an activation function. The climatic data of Redesdale climatology station, Australia for the period 2009–2012 were utilized for the analysis. The daily reference values of ET were calculated by the FAO-PM56 method. The maximum temperature, minimum temperatures and wind speed data were used as the inputs and the reference values of ET data series was utilized as the output of the ANN and WNN models. In order to assess the effect of decomposing the input data by wavelet transform on the models efficiency, the original dataset and separately the decomposed time series were applied for calibrating and validating the models. The influence of using wind speed data as the third input on the performance of models was also investigated. The results showed that both the ANN and WNN models predicted ET at an acceptable accuracy level. However, the wavlet-WNN261 (2 inputs, 6 neurons in the hidden layer and one output) performed the best with the RMSE, APE, N.S. and R values of 1.03mm/day, 22%, 0.79 and 0.89, respectively.

Suggested Citation

  • Falamarzi, Yashar & Palizdan, Narges & Huang, Yuk Feng & Lee, Teang Shui, 2014. "Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs)," Agricultural Water Management, Elsevier, vol. 140(C), pages 26-36.
    • Handle: RePEc:eee:agiwat:v:140:y:2014:i:c:p:26-36
    DOI: 10.1016/j.agwat.2014.03.014
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    References listed on IDEAS

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    2. Tianao Wu & Wei Zhang & Xiyun Jiao & Weihua Guo & Yousef Alhaj Hamoud, 2020. "Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    3. Traore, Seydou & Luo, Yufeng & Fipps, Guy, 2016. "Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages," Agricultural Water Management, Elsevier, vol. 163(C), pages 363-379.
    4. Aouissi, Jalel & Benabdallah, Sihem & Lili Chabaâne, Zohra & Cudennec, Christophe, 2016. "Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia," Agricultural Water Management, Elsevier, vol. 174(C), pages 39-51.
    5. Masoud Karbasi, 2018. "Forecasting of Multi-Step Ahead Reference Evapotranspiration Using Wavelet- Gaussian Process Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1035-1052, February.
    6. Wang, Sheng & Lian, Jinjiao & Peng, Yuzhong & Hu, Baoqing & Chen, Hongsong, 2019. "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 220-230.
    7. Wu, Zhuochun & Xiao, Liye, 2019. "A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting," Energy, Elsevier, vol. 183(C), pages 1178-1194.
    8. Kim, Ho-Jun & Chandrasekara, Sewwandhi & Kwon, Hyun-Han & Lima, Carlos & Kim, Tae-woong, 2023. "A novel multi-scale parameter estimation approach to the Hargreaves-Samani equation for estimation of Penman-Monteith reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 275(C).
    9. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).
    10. Jia Luo & Xianming Dou & Mingguo Ma, 2022. "Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    11. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.

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