Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area
文献类型：期刊论文
| 作者 | Yin, Zhenliang; Wen, Xiaohu; Feng, Qi; He, Zhibin; Zou, Songbing; Yang, Linshan |
| 刊名 | HYDROLOGY RESEARCH
 |
| 出版日期 | 2017-10-01 |
| 卷号 | 48期号:5页码:1177-1191 |
| 关键词 | climatic variables genetic algorithm reference evapotranspiration modeling semi-arid mountain areas support vector machine |
| ISSN号 | 1998-9563 |
| DOI | 10.2166/nh.2016.205 |
| 通讯作者 | Wen, Xiaohu(xhwen@lzb.ac.cn) |
| 英文摘要 | Accurate estimation of evapotranspiration is vitally important for management of water resources and environmental protection. This study investigated the accuracy of integrating genetic algorithm and support vector machine (GA-SVM) models using climatic variables for simulating daily reference evapotranspiration (ETo). The developed GA-SVM models were tested using the ETo calculated by Penman-Monteith FAO-56 (PMF-56) equation in a semi-arid environment of Qilian Mountain, northwest China. Eight models were developed using different combinations of daily climatic data including maximum air temperature (T-max), minimum air temperature (T-min), wind speed (U-2), relative humidity (RH), and solar radiation (R-s). The accuracy of the models was evaluated using root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (r). The results indicated that the GA-SVM models successfully estimated ETo with those obtained by the PMF-56 equation in the semi-arid mountain environment. The model with input combinations of Tmin, Tmax, U2, RH, and R-s had the smallest value of the RMSE and MAE as well as higher value of r (0.995) compared to other models. Relative to the performance of support vector machine (SVM) models and feed-forward artificial neural network models, it was found that the GA-SVM models proved superior for simulating ETo. |
| 收录类别 | SCI |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; FUZZY INFERENCE SYSTEM ; LIMITED CLIMATIC DATA ; DAILY PAN EVAPORATION ; ARID REGIONS ; RIVER-BASIN ; PREDICTION ; CHINA ; ANFIS ; INDEX |
| WOS研究方向 | Water Resources |
| WOS类目 | Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000412412500002 |
| 出版者 | IWA PUBLISHING |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2557696 |
| 专题 | 寒区旱区环境与工程研究所 |
| 通讯作者 | Wen, Xiaohu |
| 作者单位 | Chinese Acad Sci, Key Lab Ecohydrol Inland River Basin, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, Zhenliang,Wen, Xiaohu,Feng, Qi,et al. Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area[J]. HYDROLOGY RESEARCH,2017,48(5):1177-1191. |
| APA | Yin, Zhenliang,Wen, Xiaohu,Feng, Qi,He, Zhibin,Zou, Songbing,&Yang, Linshan.(2017).Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area.HYDROLOGY RESEARCH,48(5),1177-1191. |
| MLA | Yin, Zhenliang,et al."Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area".HYDROLOGY RESEARCH 48.5(2017):1177-1191. |
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来源：寒区旱区环境与工程研究所
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