Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.compag.2021.106211 |
Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China | |
Dong, Jianhua; Liu, Xiaogang; Huang, Guomin; Fan, Junliang; Wu, Lifeng; Wu, Jie | |
通讯作者 | Wu, LF (corresponding author), Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China. |
来源期刊 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
ISSN | 0168-1699 |
EISSN | 1872-7107 |
出版年 | 2021 |
卷号 | 186 |
英文摘要 | Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET0 across China. Monthly meteorological data [including maximum air temperature (Tmax), minimum air temperature (Tmin), extra-terrestrial solar radiation (Ra), relative humidity (RH), global solar radiation (Rs), wind speed (U)] during 1966-2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R2 = 0.9814, RMSE = 0.2143 mm d-1). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, Rs was greater than Ra, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7%, respectively), indicating a problem of overfitting. Conversely, the GWO-KNEA model was the most stable model with the smallest increase in RMSE during the testing phase (4.4-15.7%) among all models. In summary, taking the model's estimation accuracy, stability, convergence rate, and climate environment into account, our results suggest that GWO-KNEA would be a suitable model for estimating ET0 in different climate zones of China and the most practical input combination would be combination 2. |
英文关键词 | Reference evapotranspiration Climatic zones Grasshopper optimization algorithm Grey wolf optimization algorithm Machine learning |
类型 | Article |
语种 | 英语 |
收录类别 | SCI-E |
WOS记录号 | WOS:000670100500002 |
WOS关键词 | SUPPORT-VECTOR-MACHINE ; GLOBAL SOLAR-RADIATION ; ARPS DECLINE MODEL ; LIMITED DATA ; NEURO-FUZZY ; SENSITIVITY ; SIMULATION ; TRENDS ; MAIZE ; FIELD |
WOS类目 | Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications |
WOS研究方向 | Agriculture ; Computer Science |
来源机构 | 西北农林科技大学 |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/368651 |
作者单位 | [Dong, Jianhua; Huang, Guomin; Wu, Lifeng] Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China; [Liu, Xiaogang] Kunming Univ Sci & Technol, Fac Agr & Food, Kunming 650500, Yunnan, Peoples R China; [Fan, Junliang] Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid & Semiarid Are, Minist Educ, Yangling 712100, Shaanxi, Peoples R China; [Wu, Jie] Wuhan Polytech Univ, Sch Civil Engn & Architecture, Wuhan 430023, Peoples R China |
推荐引用方式 GB/T 7714 | Dong, Jianhua,Liu, Xiaogang,Huang, Guomin,et al. Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China[J]. 西北农林科技大学,2021,186. |
APA | Dong, Jianhua,Liu, Xiaogang,Huang, Guomin,Fan, Junliang,Wu, Lifeng,&Wu, Jie.(2021).Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China.COMPUTERS AND ELECTRONICS IN AGRICULTURE,186. |
MLA | Dong, Jianhua,et al."Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China".COMPUTERS AND ELECTRONICS IN AGRICULTURE 186(2021). |
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