Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data
文献类型：期刊论文
| 作者 | Nourani, Vahid3,4,5; Ahmadi, Ramin4,5; Zhang, Yongqiang2; Dabrowska, Dominika1 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2025 |
| 卷号 | 170页码:22 |
| 关键词 | Evapotranspiration PML-V2 Sensitivity analysis Machine learning Neural ensemble technique Ahar Chay Basin Seto mixed forest site |
| ISSN号 | 1470-160X |
| DOI | 10.1016/j.ecolind.2024.113012 |
| 通讯作者 | Dabrowska, Dominika(dominika.dabrowska@us.edu.pl) |
| 英文摘要 | The Penman-Monteith-Leuning version 2 (PML-V2) evapotranspiration (ET) model, an advanced iteration of the classic Penman-Monteith (PM) model, is available globally via Google Earth Engine with a spatio-temporal resolution of 500 m and 8 days. PML-V2 improves canopy conductance estimation and incorporates carbon dioxide effects on transpiration via gross primary production. However, it faces limitations, particularly in calibration and the lack of pre-2000 data. This study applies several machine learning (ML) models-including a backpropagation neural network (BPNN), an adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and long short-term memory (LSTM)-to simulate PML-V2 ET in the Ahar Chay basin, Northwestern Iran. The Seto mixed forest site in Japan, characterized by a contrasting ecosystem, served as a cross-validation site to further validate the methodology. Sensitivity analysis was performed to optimize the input variables and reduce uncertainty. Among the models, LSTM demonstrated superior performance, while an ensemble of shallow ML models increased prediction accuracy by up to 24 %. The optimal model was applied to extrapolate PML-V2 ET data for the period from 1983 to 2000. In the Ahar Chay basin, actual ET (AET) was estimated using the water balance equation, as direct observations were unavailable, and was evaluated via dynamic time warping from 2002 to 2016. Notably, neural ensemble ET and PML-V2 improved ET estimates by 55 % and 41 %, respectively, over the PM model, particularly during the growing season (April-September). At the Seto site, the methodology yielded a 39 % improvement over the PM model based on observed AET data. These findings have significant implications for ecohydrology, offering improved ET estimates for future projections and historical periods prior to 2000. |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; INFERENCE SYSTEM ANFIS ; EVAPORATION ; MODEL ; SIMULATION ; OUTPUTS ; WATER ; FLUX ; ANN |
| 资助项目 | Iran National Science Foun-dation[4021444] ; National Natural Science Founda-tion of China[42361144709] |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001391342900001 |
| 出版者 | ELSEVIER |
| 资助机构 | Iran National Science Foun-dation ; National Natural Science Founda-tion of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212495]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Dabrowska, Dominika |
| 作者单位 | 1.Univ Silesia, Fac Nat Sci, Bedzinska 60, Sosnowiec, Poland 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Near East Univ, Fac Civil & Environm Engn, Via Mersin 10, Nicosia, Turkiye 4.Univ Tabriz, Fac Civil Engn, Tabriz 166616471, Iran 5.Univ Tabriz, Ctr Excellence Hydroinformat, Tabriz 166616471, Iran |
| 推荐引用方式 GB/T 7714 | Nourani, Vahid,Ahmadi, Ramin,Zhang, Yongqiang,et al. Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data[J]. ECOLOGICAL INDICATORS,2025,170:22. |
| APA | Nourani, Vahid,Ahmadi, Ramin,Zhang, Yongqiang,&Dabrowska, Dominika.(2025).Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data.ECOLOGICAL INDICATORS,170,22. |
| MLA | Nourani, Vahid,et al."Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data".ECOLOGICAL INDICATORS 170(2025):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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