Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions
文献类型：期刊论文
| 作者 | Wen, Xiaohu; Si, Jianhua; He, Zhibin; Wu, Jun; Shao, Hongbo; Yu, Haijiao |
| 刊名 | WATER RESOURCES MANAGEMENT
 |
| 出版日期 | 2015-07-01 |
| 卷号 | 29期号:9页码:3195-3209 |
| 关键词 | Support vector machine Reference evapotranspiration modeling Limited climatic data Extreme arid regions |
| ISSN号 | 0920-4741 |
| 产权排序 | [Wen, Xiaohu; Si, Jianhua; He, Zhibin; Yu, Haijiao] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China; [Wu, Jun] Next Fuel Inc, Sheridan, WY 82801 USA; [Shao, Hongbo] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Key Lab Coastal Biol & Bioresources Utilizat, Yantai 264003, Peoples R China; [Shao, Hongbo] Jiangsu Acad Agr Sci, Inst Biotechnol, Nanjing 210014, Jiangsu, Peoples R China |
| 通讯作者 | Wen, XH (reprint author), Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, 320 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China. xhwen@lzb.ac.cn ; shaohongbochu@126.com |
| 中文摘要 | Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET0) using limited climatic data. For the SVM, four combinations of maximum air temperature (T-max ), minimum air temperature (T-min ), wind speed (U-2 ) and daily solar radiation (R-s ) in the extremely arid region of Ejina basin, China, were used as inputs with T(max)and T-min as the base data set. The results of SVM models were evaluated by comparing the output with the ET0 calculated using Penman-Monteith FAO 56 equation (PMF-56). We found that the ET0 estimated using SVM with limited climatic data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological data. In particular, three climatic parameters, T-max , T-min , and R-s were enough to predict the daily ET0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET0 with scarce data in extreme arid regions. |
| 英文摘要 | Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET0) using limited climatic data. For the SVM, four combinations of maximum air temperature (T-max ), minimum air temperature (T-min ), wind speed (U-2 ) and daily solar radiation (R-s ) in the extremely arid region of Ejina basin, China, were used as inputs with T(max)and T-min as the base data set. The results of SVM models were evaluated by comparing the output with the ET0 calculated using Penman-Monteith FAO 56 equation (PMF-56). We found that the ET0 estimated using SVM with limited climatic data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological data. In particular, three climatic parameters, T-max , T-min , and R-s were enough to predict the daily ET0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET0 with scarce data in extreme arid regions. |
| 学科主题 | Engineering, Civil; Water Resources |
| 研究领域[WOS] | Engineering ; Water Resources |
| 关键词[WOS] | NEURAL-NETWORK ; ENVIRONMENT |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000355266800010 |
| 源URL | [http://ir.yic.ac.cn/handle/133337/8499]  |
| 专题 | 烟台海岸带研究所_海岸带生物学与生物资源利用所重点实验室 |
| 推荐引用方式 GB/T 7714 | Wen, Xiaohu,Si, Jianhua,He, Zhibin,et al. Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions[J]. WATER RESOURCES MANAGEMENT,2015,29(9):3195-3209. |
| APA | Wen, Xiaohu,Si, Jianhua,He, Zhibin,Wu, Jun,Shao, Hongbo,&Yu, Haijiao.(2015).Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions.WATER RESOURCES MANAGEMENT,29(9),3195-3209. |
| MLA | Wen, Xiaohu,et al."Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions".WATER RESOURCES MANAGEMENT 29.9(2015):3195-3209. |
入库方式： OAI收割
来源：烟台海岸带研究所
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