Extreme Learning Machines: A new approach for prediction of reference evapotranspiration
Arid regions; Atmospheric temperature; Evapotranspiration; Geographical regions; Knowledge acquisition; Mean square error; Meteorology; Neural networks; Wind; Arid and semi-arid regions; Coefficient of determination; Extreme learning machine; Feedforward backpropagation; Generalization performance;...
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my.uniten.dspace-223052023-05-29T14:00:07Z Extreme Learning Machines: A new approach for prediction of reference evapotranspiration Abdullah S.S. Malek M.A. Abdullah N.S. Kisi O. Yap K.S. 57213171981 55636320055 56644103800 6507051085 24448864400 Arid regions; Atmospheric temperature; Evapotranspiration; Geographical regions; Knowledge acquisition; Mean square error; Meteorology; Neural networks; Wind; Arid and semi-arid regions; Coefficient of determination; Extreme learning machine; Feedforward backpropagation; Generalization performance; Meteorological condition; Penman-Monteith equations; Reference evapotranspiration; Learning systems; air temperature; algorithm; evapotranspiration; learning; meteorology; new record; Penman-Monteith equation; performance assessment; semiarid region; Basra; Iraq Recognizing the importance of precise determination of reference evapotranspiration (ET0) is a principal step in the attempts to reserve huge quantities of squandered water. This paper investigates the efficiency of Extreme Learning Machines (ELM) algorithm at predicting Penman-Monteith (P-M) ET0 for Mosul, Baghdad, and Basrah meteorological stations, located at the north, mid, and southern part of Iraq. Data of weather parameters containing maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh), and wind speed (U2) for the period (2000-2013) are used as inputs to the ELM model by using four different input cases including complete and incomplete sets of meteorological data. The performance of ELM model is compared with the empirical P-M equation and with feedforward backpropagation (FFBP) model. The evaluation criteria used for comparison are the root of mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The statistical results of both models are found to be encouraging; particularly results of running the ELM model with incomplete sets of data, noticing that the sensitivity of the proposed model to missing data changes from one location to another, as well as along the year for certain study location. The Rn is found to be the most effective parameter in Mosul Station, while U2 and Rh are found to act almost in parallel with Rn in Baghdad Station, and for conditions of Basrah Station; U2 and Rh prove to be the dominant parameters. The minimum and maximum time intervals required for running ELM model for all stations, and in all applied conditions, are (4.64-6.19) seconds respectively, while the same order of timing required for running the FFBP model is (6.30-27.80) seconds. The maximum R2 recorded for the ELM model is 0.991, while for the FFBP it is 0.985. The ELM proved to be efficient, simple in application, of high speed, and has very good generalization performance; therefore, this algorithm is highly recommended for locations similar to the geographical and meteorological conditions of Iraq that consists of both arid and semiarid regions. � 2015 Elsevier B.V. Final 2023-05-29T06:00:07Z 2023-05-29T06:00:07Z 2015 Article 10.1016/j.jhydrol.2015.04.073 2-s2.0-84929299317 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929299317&doi=10.1016%2fj.jhydrol.2015.04.073&partnerID=40&md5=d5510fe0c79580cee795fe35836bbccd https://irepository.uniten.edu.my/handle/123456789/22305 527 184 195 Elsevier Scopus |
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Arid regions; Atmospheric temperature; Evapotranspiration; Geographical regions; Knowledge acquisition; Mean square error; Meteorology; Neural networks; Wind; Arid and semi-arid regions; Coefficient of determination; Extreme learning machine; Feedforward backpropagation; Generalization performance; Meteorological condition; Penman-Monteith equations; Reference evapotranspiration; Learning systems; air temperature; algorithm; evapotranspiration; learning; meteorology; new record; Penman-Monteith equation; performance assessment; semiarid region; Basra; Iraq |
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57213171981 |
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57213171981 Abdullah S.S. Malek M.A. Abdullah N.S. Kisi O. Yap K.S. |
| format |
Article |
| author |
Abdullah S.S. Malek M.A. Abdullah N.S. Kisi O. Yap K.S. |
| spellingShingle |
Abdullah S.S. Malek M.A. Abdullah N.S. Kisi O. Yap K.S. Extreme Learning Machines: A new approach for prediction of reference evapotranspiration |
| author_sort |
Abdullah S.S. |
| title |
Extreme Learning Machines: A new approach for prediction of reference evapotranspiration |
| title_short |
Extreme Learning Machines: A new approach for prediction of reference evapotranspiration |
| title_full |
Extreme Learning Machines: A new approach for prediction of reference evapotranspiration |
| title_fullStr |
Extreme Learning Machines: A new approach for prediction of reference evapotranspiration |
| title_full_unstemmed |
Extreme Learning Machines: A new approach for prediction of reference evapotranspiration |
| title_sort |
extreme learning machines: a new approach for prediction of reference evapotranspiration |
| publisher |
Elsevier |
| publishDate |
2023 |
| _version_ |
1806424417112162304 |
| score |
13.214268 |