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Can I read Evapotranspiration Estimation Using Hybrid and Intelligent Methods on EtoBox?
Evapotranspiration Estimation Using Hybrid and Intelligent Methods by Amin Amirashayeri; Javad Behmanesh; Vahid Rezaverdinejad; Nasrin Fathollahzadeh Attar is a Computer Science article available to read on EtoBox.
What is Evapotranspiration Estimation Using Hybrid and Intelligent Methods about?
Accurate prediction of reference evapotranspiration (ET 0 ) is very valuable since it directly affects the amount of agricultural water needed, the allocation of water consumption and the management of irrigation systems. This research consists of two parts; in the first part, ET 0 was predicted by two machine learning models (artificial neural network (ANN)) and tree model (MT) with two empirical equations (Romanenko and Schendel). In the second part, a new preprocessing algorithm, ensemble empirical mode decomposition (EEMD), was used to improve the prediction accuracy of the MT model and eliminate timeseries noises. This approach was applied to several study areas in northwest of Iran including Urmia, Mahabad, Khoy, Takab, and Sardasht stations so that daily reference evapotranspiration values were calculated based on FAO-Penman equation. The parameters of average temperature, average relative humidity, average vapor pressure and average wind speed were input parameters to the models. The results of the first part showed that the performance of MT (with RMSE: 0.58 mm/day, RSD: 0.18, NSE: 0.97) has better performance than the ANN model (with RMSE: 0.70 mm/day, RSD: 0.22, NSE: 0.9
Who reads Evapotranspiration Estimation Using Hybrid and Intelligent Methods?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Amin Amirashayeri; Javad Behmanesh; Vahid Rezaverdinejad; Nasrin Fathollahzadeh Attar
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Computer Science (Physical Sciences)