hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO
Reference evapotranspiration (ETo), one of the key elements of the hydrological cycle, is crucial for managing irrigation and drainage systems. In order to estimate monthly ETo, this study tested the prediction abilities of a unique hybrid methodology that coupled data pre-processing with a hybrid m...
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| Format: | Article |
| Language: | English |
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Wasit University
2023-12-01
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| Series: | Wasit Journal of Engineering Sciences |
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| Online Access: | https://ejuow.uowasit.edu.iq/index.php/ejuow/article/view/450 |
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| author | Hadeel Essa Salah L. Zubaidi |
| author_facet | Hadeel Essa Salah L. Zubaidi |
| author_sort | Hadeel Essa |
| collection | DOAJ |
| description | Reference evapotranspiration (ETo), one of the key elements of the hydrological cycle, is crucial for managing irrigation and drainage systems. In order to estimate monthly ETo, this study tested the prediction abilities of a unique hybrid methodology that coupled data pre-processing with a hybrid model composed of an artificial neural network (ANN) and particle swarm optimisation (PSO). In order to train and evaluate the model, monthly meteorological data were collected in Al-Kut City, Iraq, from 1990 to 2020. A range of statistical indicators were used to assess the model, including RMSE, NSE, and R2. The outcomes showed that the model, with a coefficient of determination of 0.93, is effective and has good simulation levels.
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| format | Article |
| id | doaj-art-c794587ec62f44f9aaad00f29adb522e |
| institution | DOAJ |
| issn | 2305-6932 2663-1970 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Wasit University |
| record_format | Article |
| series | Wasit Journal of Engineering Sciences |
| spelling | doaj-art-c794587ec62f44f9aaad00f29adb522e2025-08-20T02:39:25ZengWasit UniversityWasit Journal of Engineering Sciences2305-69322663-19702023-12-0111310.31185/ejuow.Vol11.Iss3.450hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSOHadeel Essa0Salah L. Zubaidi1Wasit University-Engineering CollegeDepartment of Civil Engineering, Wasit UniversityReference evapotranspiration (ETo), one of the key elements of the hydrological cycle, is crucial for managing irrigation and drainage systems. In order to estimate monthly ETo, this study tested the prediction abilities of a unique hybrid methodology that coupled data pre-processing with a hybrid model composed of an artificial neural network (ANN) and particle swarm optimisation (PSO). In order to train and evaluate the model, monthly meteorological data were collected in Al-Kut City, Iraq, from 1990 to 2020. A range of statistical indicators were used to assess the model, including RMSE, NSE, and R2. The outcomes showed that the model, with a coefficient of determination of 0.93, is effective and has good simulation levels. https://ejuow.uowasit.edu.iq/index.php/ejuow/article/view/450Reference evapotranspirationANNPSOMIAl Kut |
| spellingShingle | Hadeel Essa Salah L. Zubaidi hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO Wasit Journal of Engineering Sciences Reference evapotranspiration ANN PSO MI Al Kut |
| title | hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO |
| title_full | hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO |
| title_fullStr | hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO |
| title_full_unstemmed | hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO |
| title_short | hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO |
| title_sort | hybrid model to improve reference evapotranspiration prediction integrating ann and pso |
| topic | Reference evapotranspiration ANN PSO MI Al Kut |
| url | https://ejuow.uowasit.edu.iq/index.php/ejuow/article/view/450 |
| work_keys_str_mv | AT hadeelessa hybridmodeltoimprovereferenceevapotranspirationpredictionintegratingannandpso AT salahlzubaidi hybridmodeltoimprovereferenceevapotranspirationpredictionintegratingannandpso |