Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.jhydrol.2015.04.073 |
Extreme Learning Machines: A new approach for prediction of reference evapotranspiration | |
Abdullah, Shafika Sultan1,6; Malek, M. A.1; Abdullah, Namiq Sultan2; Kisi, Ozgur3; Yap, Keem Siah4,5 | |
通讯作者 | Abdullah, Shafika Sultan |
来源期刊 | JOURNAL OF HYDROLOGY
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ISSN | 0022-1694 |
EISSN | 1879-2707 |
出版年 | 2015 |
卷号 | 527页码:184-195 |
英文摘要 | 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 (T-max), minimum air temperature (T-min), sunshine hours (R-n), relative humidity (R-h), and wind speed (U-2) 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 (R-2). 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 R-n is found to be the most effective parameter in Mosul Station, while U-2 and R-h are found to act almost in parallel with R-n in Baghdad Station, and for conditions of Basrah Station; U-2 and R-h 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 R-2 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. (C) 2015 Elsevier B.V. All rights reserved. |
英文关键词 | Reference evapotranspiration Penman-Monteith equation Extreme Learning Machines Artificial Neural Networks |
类型 | Article |
语种 | 英语 |
国家 | Malaysia ; Iraq ; Turkey |
收录类别 | SCI-E |
WOS记录号 | WOS:000358629100018 |
WOS关键词 | NEURAL-NETWORKS ; CLASSIFICATION ; MODELS |
WOS类目 | Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources |
WOS研究方向 | Engineering ; Geology ; Water Resources |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/188788 |
作者单位 | 1.Univ Tenaga Nas, Dept Civil Engn, Kajang 43000, Selangor, Malaysia; 2.Univ Duhok, Dept Elect & Comp Engn, Duhok, Iraq; 3.Canik Basari Univ, Dept Civil Engn, Fac Engn & Architecture, Samsun, Turkey; 4.Univ Tenaga Nas, Dept Elect & Commun Engn, Kajang 43000, Selangor, Malaysia; 5.Univ Tenaga Nas, Coll Grad Studies, Kajang 43000, Selangor, Malaysia; 6.Dohuk Polytech Univ, Akre Tech Inst, Dohuk, Iraq |
推荐引用方式 GB/T 7714 | Abdullah, Shafika Sultan,Malek, M. A.,Abdullah, Namiq Sultan,et al. Extreme Learning Machines: A new approach for prediction of reference evapotranspiration[J],2015,527:184-195. |
APA | Abdullah, Shafika Sultan,Malek, M. A.,Abdullah, Namiq Sultan,Kisi, Ozgur,&Yap, Keem Siah.(2015).Extreme Learning Machines: A new approach for prediction of reference evapotranspiration.JOURNAL OF HYDROLOGY,527,184-195. |
MLA | Abdullah, Shafika Sultan,et al."Extreme Learning Machines: A new approach for prediction of reference evapotranspiration".JOURNAL OF HYDROLOGY 527(2015):184-195. |
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