Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/joc.5064 |
Evaporation modelling using different machine learning techniques | |
Wang, Lunche1,2; Kisi, Ozgur3; Hu, Bo2; Bilal, Muhammad4; Zounemat-Kermani, Mohammad5; Li, Hui1 | |
2017-08-01 | |
发表期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY |
ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2017 |
卷号 | 37 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Georgia; Iran |
英文摘要 | Accurate prediction of pan evaporation (Ep) is critical for water resource management. This article investigates the capabilities of three different soft computing methods at estimating monthly Ep at six stations in the Yangtze River Basin using climatic factors, including the air temperature (Ta), solar radiation (Rg), air pressure (Pa) and wind speed (Ws) for the period of 1961-2000. The first part of the study focused on testing and comparing model accuracy levels at each station using local input combinations. The results indicate that the fuzzy genetic (FG) model generally produces better results than adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP) and M5 model tree (M5Tree) specifications in terms of the root mean square error, mean absolute error and coefficient of determination values. The performance of the above models was also examined using cross-station applications (estimating Ep without local input or output data) in the second part of the study. The third part focused on estimating Ep using generalized FG, ANFIS-GP and M5 Tree models. Collectively, the results demonstrate that the FG model can be successfully used to estimate Ep without any local inputs and outputs and that a single generalized FG model can also be used at six different locations. |
英文关键词 | pan evaporation fuzzy genetic algorithm ANFIS-GP M5 model tree cross-station application |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000417298600072 |
WOS关键词 | ESTIMATING REFERENCE EVAPOTRANSPIRATION ; CHANGING PAN EVAPORATION ; ADAPTIVE NEURO-FUZZY ; OPEN-WATER ; TRENDS ; CHINA ; TREE ; ALGORITHM ; CLIMATES ; REGION |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/36894 |
专题 | 气候变化 |
作者单位 | 1.China Univ Geosci, Sch Earth Sci, Lab Crit Zone Evolut, Luoyu Rd, Wuhan 430074, Hubei, Peoples R China; 2.Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Atmospher Boundary Phys & Atmospher, Beijing, Peoples R China; 3.Int Black Sea Univ, Ctr Interdisciplinary Res, Tbilisi, Georgia; 4.Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Kowloon, Hong Kong, Peoples R China; 5.Shahid Bahonar Univ Kerman, Dept Water Engn, Kerman, Iran |
推荐引用方式 GB/T 7714 | Wang, Lunche,Kisi, Ozgur,Hu, Bo,et al. Evaporation modelling using different machine learning techniques[J]. INTERNATIONAL JOURNAL OF CLIMATOLOGY,2017,37. |
APA | Wang, Lunche,Kisi, Ozgur,Hu, Bo,Bilal, Muhammad,Zounemat-Kermani, Mohammad,&Li, Hui.(2017).Evaporation modelling using different machine learning techniques.INTERNATIONAL JOURNAL OF CLIMATOLOGY,37. |
MLA | Wang, Lunche,et al."Evaporation modelling using different machine learning techniques".INTERNATIONAL JOURNAL OF CLIMATOLOGY 37(2017). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论