[1]BAO Lingling,YANG Yonggang,LIU Jianjun,et al.Calculation of Reference Crop Evapotranspiration in Chongqing Based on 5 Artificial Intelligent Models[J].Research of Soil and Water Conservation,2021,28(01):85-92.
Copy
Calculation of Reference Crop Evapotranspiration in Chongqing Based on 5 Artificial Intelligent Models
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
28
Number of periods:
2021 01
Page number:
85-92
Column:
Public date:
2021-01-10
- Title:
- Calculation of Reference Crop Evapotranspiration in Chongqing Based on 5 Artificial Intelligent Models
- Keywords:
- Chongqing; reference crop evapotranspiration; artificial intelligent models; empirical model; sunshine hours
- CLC:
- S161.4
- DOI:
- -
- Abstract:
- In order to obtain the optimal model for calculating reference crop evapotranspiration(ET0)in Chongqing, the five artificial intelligent models such as support vector machines(SVM), Gaussian exponential model(GEM), random forest model(RF), extreme learning machine model(ELM)and generalized regression neural network model(GRNN)were used as the calculation models. Based on the daily meteorological data from Fengdu, Fengjie, Shapingba, Wanzhou, and Youyang from 1991 to 2016, the daily and monthly ET0 under different combinations of meteorological parameter inputs were estimated, and compared with the calculation results of the standard model Penman-Monteith(PM). The results show that the accuracies of different models are different; under the input of the same meteorological parameters, the calculation accuracy of the artificial intelligence model is higher than that of the empirical model; the error index of the GEM model is the lowest and the consistency index is the highest. Sunshine duration is the most critical factor impacting the accuracy of modeling and change in ET0 in Chongqing. GEM model is the optimal artificial intelligence model to estimate ET0 in Chongqing.
- References:
-
[1] Xu C Y, Singh V P. Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions[J]. Journal of Hydrology, 2005,308(1/4):105-121.
[2] Wang Q, Xu Y P, Wang Y F, et al. Individual and combined impacts of future land-use and climate conditions on extreme hydrological events in a representative basin of the Yangtze River Delta, China[J]. Atmospheric Research, 2020,236:1-14.
[3] 张雅芳,郭英,沈彦俊,等.华北平原种植结构变化对农业需水的影响[J].中国生态农业学报,2020,28(1):8-16.
[4] 黄航行,李思恩.1968―2018年民勤地区参考作物需水量的年际变化特征及相关气象影响因子研究[J].灌溉排水学报,2019,38(12):63-67.
[5] 李丰琇,马英杰.基于双作物系数法的新疆覆膜滴灌夏玉米蒸散量估算[J].农业机械学报,2018,49(11):268-274.
[6] 曹永强,朱明明,李维佳.河北省典型区主要作物有效降雨量和需水量特征[J].生态学报,2018,38(2):560-570.
[7] Allen R G, Pereira L S, Raes D, et al. Crop evapotranspirationguidelines for computing crop water requirements[M]. Rome:Food and Agriculture Organization of United Nation, 1998.
[8] 娄忠秋,李桢.不同简化算法模型模拟都江堰灌区参考作物蒸散量[J].水土保持研究,2019,26(5):272-277,286.
[9] 冯禹,崔宁博,龚道枝.机器学习算法和Hargreaves模型在四川盆地ET0计算中的比较[J].中国农业气象,2016,37(4):415-421.
[10] 李晨,崔宁博,冯禹,等.四川省不同区域参考作物蒸散量计算方法的适用性评价[J].农业工程学报,2016,32(4):127-134,316.
[11] 李晨,李王成,赵自阳,等.宁夏引黄灌区几种参考作物蒸散量计算方法适用性及修正研究[J].中国农村水利水电,2019(11):54-59,65.
[12] 张薇,霍树义,贾悦.机器学习模型在河北省参考作物蒸散量计算中的比较[J].节水灌溉,2018(4):50-53,58.
[13] 陈宣全,崔宁博,李继平,等.多元自适应回归样条算法模拟川中丘陵区参考作物蒸散量[J].农业工程学报,2019,35(16):152-160.
[14] 魏俊,崔宁博,陈雨霖,等.基于极限学习机模型的中国西北地区参考作物蒸散量预报[J].中国农村水利水电,2018(8):35-39.
[15] 徐颖,张皓杰,崔宁博,等.基于不同ELM的西北旱区参考作物蒸散量模拟模型[J].中国农村水利水电,2019(1):6-12.
[16] 冯禹,王守光,崔宁博,等.基于遗传算法优化神经网络的参考作物蒸散量预测模型[J].资源科学,2014,36(12):2624-2630.
[17] 贾悦,崔宁博,魏新平,等.基于反距离权重法的长江流域参考作物蒸散量算法适用性评价[J].农业工程学报,2016,32(6):130-138.
[18] Vapink V. The nature of statistical learning theory[M]. New York:Springer-Verlag, 1999.
[19] Liu C, Zheng D, Zhao L, et al. Gaussian fitting for carotid and radial artery pressure waveforms:comparison between normal subjects and heart failure patients[J]. Bio-Medical Materials and Engineering, 2014,24(1):271-277.
[20] Buja A, Swayne D, Littman M, et al. Data visualization with multidimensional scaling[J]. Journal of Computational and Graphical Statistics, 2008,17(2):444-472.
[21] 冯禹,崔宁博,龚道枝,等.基于极限学习机的参考作物蒸散量预测模型[J].农业工程学报,2015,31(S1):153-160.
[22] 王小川,史峰,郁磊,等. MATLAB神经网络43个案例分析[M].北京:北京航空航天大学出版社,2013.
[23] Desideri U, Zepparelli F, Morettini V, et al. Comparative analysis of concentrating solar power and photovoltaic technologies:technical and environmental evaluations[J]. Appl Energy, 2013,102:765-784.
[24] 刘小华,魏炳乾,吴立峰,等.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,38(1):102-108.
[25] 邢立文,崔宁博,董娟.基于LSTM深度学习模型的华北地区参考作物蒸散量预测研究[J].水利水电技术,2019,50(4):64-72.
[26] Lesser B, Mücke M, Gansterer W N. Effects of reduced precision on floating-point SVM classification accuracy[J]. Procedia Computer Science, 2011,4:508-517.
[27] Xu T R, Guo Z X, Xia Y L, et al. Ferreira, Shaomin Liu, Kaicun Wang, Yunjun Yao, Xiaojuan Zhang, Changsen Zhao. Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States[J]. Journal of Hydrology, 2019,578:1-12.
[28] Corinne C, Liang S L. Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing[J]. International Journal of Applied Earth Observations and Geoinformation, 2019,78:1-9.
[29] 贾悦,崔宁博,魏新平,等.气候变化与灌溉对都江堰灌区参考作物蒸散量影响研究[J].四川大学学报:工程科学版,2016,48(S1):69-79.
- Similar References:
Memo
-
Last Update:
2021-01-15