基于GA-Elman神经网络的参考作物需水量预测
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摘要
根据通东灌区多年气象资料以及逐日参考作物需水量数据,构建了以日序数、日照时数、日平均气温等因子为输入向量,以逐日参考作物需水量为输出向量的GA-Elman神经网络参考作物需水量预测模型,通过随机选取方式,采用通东灌区2015年逐日气象资料及参考作物需水量等数据对模型进行了测试,结果表明:构建的GA-Elman模型具有较高的预测性能精度,其相对误差绝对值均值为7.24%,绝大多数处于0%～25%范围内,其中处于0%～10%范围内的占总数81.8%,模型具有较强的实际应用价值。
Based on the many years of meteorological data and daily reference crop water demand data in Tong Dong irrigated area,the GA-Elman neural network reference crop water demand forecasting model,with daily number,sunshine time and daily mean temperature as input vectors and daily reference crop water demand as the output vector,was constructed. The model was tested with the data of day by day meteorological data and the water requirement of reference crop of 2015 in Tong Dong irrigation area. The results show that the GA-Elman model has high predictive precision,the mean relative error of absolute value is 7.24%,the overwhelming majority is in the 0% ～ 25% range,and the total number in the 0 ～ 10% range is 81.8%. It has a strong practical application value.
Based on the many years of meteorological data and daily reference crop water demand data in Tong Dong irrigated area,the GA-Elman neural network reference crop water demand forecasting model,with daily number,sunshine time and daily mean temperature as input vectors and daily reference crop water demand as the output vector,was constructed. The model was tested with the data of day by day meteorological data and the water requirement of reference crop of 2015 in Tong Dong irrigation area. The results show that the GA-Elman model has high predictive precision,the mean relative error of absolute value is 7.24%,the overwhelming majority is in the 0% ～ 25% range,and the total number in the 0 ～ 10% range is 81.8%. It has a strong practical application value.
引文
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[2]高瑞忠,朝伦巴更,于婵,等.基于随机样本的神经网络模型估算参考作物腾发量[J].农业工程学报,2006,22(2):42-45.
[3] Anctil Francois,Michel Claude,Perrin Charles,et al. A soil mois-ture index as an auxiliary ANN input for stream flow forecasting[J].Journal of Hydrology,2004,286(4):155-167.
[4]徐俊增,彭世彰,张瑞美,等.基于气象预报的参考作物蒸发蒸腾量的神经网络模型[J].水利学报,2006,37(3):376-379.
[5] Yuan H C,Xiong F L,Huai X Y. A method for estimating the num-ber of hidden neurons in feed-forward neural networks based on infor-mation entropy[J]. Computer and Electronics in Agriculture,2003,40:57-64.
[6]买苗,邱新法,曾燕.西部部分地区农田实际蒸散量分布特征[J].中国农业气象,2004,25(4):28-32.
[7]王笑影,梁文举,文大中.北方稻田蒸散量需水分析及其作物系数确定[J].应用生态学报,2005,16(1):69-72.
[8] XIONG L H,GUO S L. Trend test and change-point detection for theannual discharge series of the Yangtze River at the Yichang hydrologi-cal station[J]. Hydrological Sciences Journal,2004,49(1),99-112.
[9] Yue S,Pilon P,Cavadias G. Power of the Mann-Kendall and Spam-an's test for detecting monotonic trends in hydrological series[J].Journal of Hydrology,2002(259):254-271.
[10] Tokinaga S,Moriyasu H,Miyazaki A,et al. Forecasting of time se-ries with fractal geometry by using scale transformationg sand parame-ter estimations obtained by the wavelet transform[J]. Electronic sandcommunications in Japan,Part3,2002,80(9):2 054-2 062.
[11] Singh V P,Xu C Y. Evaluation and generalization of 13 mass-trans-fer equatons for deltemining free water evaporation[J]. HydrologicalProcesses,1997,(11):311-324.
[12] Moriasi D N,Arnold J G,Van Liew M W,et al. Model evaluationguidelines for systematic quantification of accuracy in watershed sim-ulations[J]. Transactions of the ASABE,2007,50(3):885-900.
[13]胡平,康玲.一种非线性组合预测方法在径流预测中的应用[J].中国农村水利水电,2004,(3):38-40.
[14]蒋晓辉,刘昌明.基于小波分析的径向基神经网络年径流预测[J].应用科学学报,2004,22(3):411-414.
[15]李鹏鹏,苏怀智,郭芝韵,等.基于人工蜂群算法与Elman神经网络的大坝变形监控模型[J].水利水电技术,2017,48(3):104-108.
[16] Kizhener S,Blank K,Flatlely T,ea al. On certain theoretical devel-opments underlying the Hilbert-Huang transform[C]∥AerospaceConference,2006:1-14.
[17] FAO-Food,Agriculture Organization. Crop evapotranspiration-guidelines for computing crop water requirements[M]. Rome:FAO-Food and Agriculture Organization Press,1998.