Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure

Ahmed El-Shafie, Humod Mosad Alsulami, Heerbod Jahanbani, Ali Najah

    Research output: Contribution to journalArticle

    17 Citations (Scopus)

    Abstract

    Obtaining an accurate estimate of the reference evapotranspiration (ETo) can be difficult, especially when there is insufficient data to utilize the Penman-Monteith method. Artificial intelligence-based methods may provide reliable prediction models for several applications in engineering. However, time-series prediction based on artificial neural network (ANN) learning algorithms is fundamentally problematic. For example, the ANN model can experience over-fitting during training and, in consequence, lose its generalization. In this research, several over-fitting procedures have been augmented with the classical ANN model, are proposed. This model was applied to the prediction of the daily ETo at Rasht city, located in the north part of Iran, by using the minimum and maximum daily temperature of the region collected from 1975-1988. In addition, three different scenarios have been developed in order to achieve better prediction accuracy. The results showed that the proposed ENN model successfully predicted the daily ETo with a significant level of accuracy using only the maximum and minimum temperatures. The model also outperformed the classical ANN method. In addition, the proposed ENN compared with Hargreaves and Samani (Appl Eng Agric 1:96-99, 1985) (HGS) model and showed the ENN provides more accurate prediction for ETo. Furthermore, the proposed model could provide relatively good level of accuracy when examined for multi-lead predictions, which could not be afford by HGS model.

    Original languageEnglish
    Pages (from-to)1423-1440
    Number of pages18
    JournalStochastic Environmental Research and Risk Assessment
    Volume27
    Issue number6
    DOIs
    Publication statusPublished - Aug 2013

    Fingerprint

    Evapotranspiration
    artificial neural network
    evapotranspiration
    Lead
    Neural networks
    prediction
    artificial intelligence
    Learning algorithms
    Artificial intelligence
    Time series
    learning
    temperature
    time series
    engineering
    Temperature

    Keywords

    • Ensemble neural network
    • Evapotranspiration
    • Neural network
    • Over-fitting
    • Rasht City (Iran)

    ASJC Scopus subject areas

    • Environmental Engineering
    • Environmental Science(all)
    • Environmental Chemistry
    • Water Science and Technology
    • Safety, Risk, Reliability and Quality

    Cite this

    Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure. / El-Shafie, Ahmed; Alsulami, Humod Mosad; Jahanbani, Heerbod; Najah, Ali.

    In: Stochastic Environmental Research and Risk Assessment, Vol. 27, No. 6, 08.2013, p. 1423-1440.

    Research output: Contribution to journalArticle

    El-Shafie, Ahmed ; Alsulami, Humod Mosad ; Jahanbani, Heerbod ; Najah, Ali. / Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure. In: Stochastic Environmental Research and Risk Assessment. 2013 ; Vol. 27, No. 6. pp. 1423-1440.
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