Applicability of four kinds of artificial intelligent models to prediction of reference crop evapotranspiration in Jiangxi province
LIU Xiaohua1, WEI Bingqian1*, WU Lifeng2, YANG Po1
1. State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi′an University of Technology, Xi′an, Shaanxi 710048, China; 2. National and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang, Jiangxi 330099, China
Abstract：A highly precise estimate of reference crop evapotranspiration(ET0)in absence of some meteorological data is on demand. Based on daily maximum and minimum ambient temperatures Tmax and Tmin, sunshine hours n, relative humidity, RH, and wind speed at 2 m height, u2, during 1966—2015 in Nanchang, Ji′an and Longnan meteorological stations in Jiangxi province, four artificial intelligent(AI)models for predicting ET0 are established in terms of different combinations of six meteorological elements by using FAO-56 Penman-Monteith(P-M)formula as standard. The predicted results are compared with those calculated by empirical method. The results show that the MARS model has the highest accuracy in three stations but also its computation procedure is simple; eventually, it is the recommended method for estimating ET0 in the province. If the input data are complete, four mo-dels can achieve the best accuracy, indicating all the models are applicable to ET0 prediction. In absence of some input data, the influence of meteorological elements on ET0 estimation from the most important to the least important is as follows: Tmax>Tmin>n>RH>u2. Compared with the traditional empirical formulas, the accuracy of four AI models is better for the same input data.
刘小华,魏炳乾*,吴立峰,杨坡. 4种人工智能模型在江西省参考作物蒸散量计算中的适用性[J]. 排灌机械工程学报, 2020, 38(1): 102-108.
LIU Xiaohua,WEI Bingqian*,WU Lifeng,YANG Po. Applicability of four kinds of artificial intelligent models to prediction of reference crop evapotranspiration in Jiangxi province. Journal of Drainage and Irrigation Machinery Engin, 2020, 38(1): 102-108.
