基于神经网络算法的广东省典型代表站点ET_0简化计算模型研究

【目的】探讨BP、极限学习机、小波神经网络算法在广东典型气候代表站点的适用性,建立ET_0简化计算模型。【方法】以韶关、深圳、广州、揭西、湛江站为研究对象,收集各站1981—2010年逐日平均、最高、最低气温、相对湿度、日照时间、风速观测数据,以FAO-56Penman-Monteith ET_0计算值为基准,对比3种算法计算效结果,确定最优算法,并结合因子敏感性分析建立了ET_0简化计算模型。【结果】①P<0.05显著水平下,广州、韶关站各气象因子均差异显著;湛江、广州、揭西、深圳4站除日最高气温差异显著,其他气象因子差异均不显著;②ET_0因子敏感性分析中,韶关、广州、深圳3站日最低、最高气温、日照时间敏感系数较大,韶关站为0.040、0.113、0.223,广州站为0.043、0.101、0.208,深圳站为0.054、0.105、0.181;揭西和湛江站日最高气温、相对湿度、日照时间敏感系数较大,分别为:0.105、-0.040、0.216和0.098、-0.072、0.197,综合各站来看,日最高气温、日照时间最为敏感;③全因子输入条件下,ET_0计算精度表现为BP>极限学习机>小波神经网络;④ET_0简化计算精度表现为BP(全因子输入)>BP-1(日最高、最低气温,相对湿度,日照时间作输入)>BP-2(日最高气温、日照时间输入),但差值不大。【结论】因此,基于日最高气温、日照时间2因素的BP算法一定程度能简化计算ET_0。

Using Neural Network Model to Simplify ET_0 Calculation for Representative Stations in Guangdong Province

【Objective】The purpose of this paper to investigate the feasibility of using BP, wavelet neural network and learning machine algorithm to differentiate the factors which affect the evapotranspiration at representative stations in Guangdong province.【Method】We selected stations at Shaoguan, Shenzhen, Guangzhou, Jiexi and Zhanjiang as examples; and analyzed daily verage temperature, the highest and the lowest temperature, relative humidity, sunshine duration and wind speed, measured from 1981 to 2010 in these stations. The optimal estimation was determined by comparing the ET_0 estimated using the FAO-56 penman Monteith with those calculated using the above three methods. The simplified model for ET_0 was then established using factor sensitivity analysis.【Result】① The meteorological factors in Guangzhou and Shaoguan differed significantly at P=0.05 level.Apart from daily maximum temperature, other meteorological factors in Zhanjiang, Guangzhou, Jiexi and Shenzhen did not show significant difference. ②The factor sensitivity analysis revealed that ET_0 at Shaoguan, Guangzhou and Shenzhen was sensitive to the lowest and the highest daily temperature and the sunshine hours, with the sensitive coefficient associated with the three factors for Shaoguan being 0.040, 0.113 and 0.223, for Guangzhou being 0.043, 0.101 and 0.208, and for Shenzhen being 0.054, 0.105 and 0.18. ET_0 at Jiexi and Zhanjiang was most sensitive to the highest temperature, relative humidity and sunshine duration, with the sensitive coefficient associated with them being 0.105,-0.040 and 0.216 for Jiexi, and 0.098,-0.072 and 0.197.3 for Zhanjiang. ③Considering all factors, the accuracy of the proposed methods was ranked in BP> limit learning machine> wavelet neural network. ④ After simplification, the accuracy of the methods was ranked in BP0(considering all factors) >BP1(considering only daily highest temperature, lowest temperature, relative humidity and sunshine duration) >BP2(considering only daily highest temperature, sunshine duration).【Conclusion】ET_0 calculation can be simplified by the BP algorithm based on the highest daily temperature and sunshine duration.