Abstract:Forecasting crop water requirement is an important basis for formulating a reasonable irrigation system. In view of the deficiencies of BP neural network, genetic algorithm(GA)has the characteristics of strong global search capability, and a prediction model of crop water requirement based on GA-BP neural network was established. Taking the experimental crops of the Lamu Farm in Guangzhou as the object of research, the results show that the mean square error and certainty coefficient of the test set of crop water requirement prediction model based on BP neural network are 0.037 and 0.648 respectively. The square error and the certainty coefficient of the rest set of crop water demand forecasting model based on the GA-BP neural network are 0.013 and 0.882 resiectively. The GA-BP crop water demand forecasting model has a convergence rate, certainty coefficient and performance better than the BP crop water demand forecasting model.
[1]孙景生,康绍忠.我国水资源利用现状与节水灌溉发展对策[J].农业工程学报, 2000, 16(2):1-5. SUN Jingsheng, KANG Shaozhong. Present situation of water resources usage and developing countermeasures of water-saving irrigation in China [J]. Transactions of the CSAE, 2000, 16(2):1-5.(in Chinese)[2]于婵, 朝伦巴根, 高瑞忠,等. 作物需水量模拟计算结果有效性检验[J]. 农业工程学报, 2009, 25(12):13-21. YU Chan, CHAOLUNBAGEN,GAO Ruizhong, et al. Validity examination of simulated results of crop water requirements [J]. Transactions of the CSAE, 2009, 25(12):13-21.(in Chinese)[3]陈武奋,张倩华,黄钲武,等.基于回归型支持向量机的温泉水质pH值预测研究[J].人民珠江,2016,37(7):94-97. CHEN Wufen, ZHANG Qianhua, HUANG Zhengwu, et al. Prediction of pH value of hot springs water quality based on regression support vector machine [J]. People′s Pearl River, 2016,37(7): 94-97.(in Chinese)[4]刘婧然,马英杰,王喆,等.基于RBF神经网络与BP神经网络的核桃作物需水量预测[J].节水灌溉,2013(3):16-19. LIU Jingran, MA Yingjie, WANG Zhe, et al. Prediction of water requirement of walnut crops based on RBF neural network and BP neural network [J]. Water saving irrigation, 2013(3): 16-19.(in Chinese)[5]ALLEN R G, PEREIRA L S, Raes D, et al. Crop evapo-transpiration-Guidelines for computing crop water requirements-FAO irrigation and drainage paper 56 [M]. FAO, Rome:FAO, 2014.[6]GRISMER M E, ORANG M, SNYDER R, et al. Pan evaporation to reference evapotranspiration conversion methods[J]. Journal of irrigation & drainage enginee-ring, 2002, 128(3):180-184.[7]夏泽豪,翁绍捷,罗微,等.基于灰色神经网络的作物需水量预测模型研究[J].中国农机化学报,2015,36(2):219-223. XIA Zehao, WENG Shaojie, LUO Wei, et al. Research on crop water demand prediction model based on grey neural network [J]. Chinese agricultural machinery chemistry report,2015,36(2):219-223.(in Chinese)[8]商志根.基于神经网络集成的作物需水量预测[J].软件导刊,2018,17(1):46-48. SHANG Zhigen. Prediction of crop water requirements based on neural network ensemble[J].Software guide, 2018,17(1): 46-48.(in Chinese)[9]LI S, KANG S, LI F, et al. Evapotranspiration and crop coefficient of spring maize with plastic mulch using eddy covariance in northwest China[J]. Agricultural water management, 2008, 95(11):1214-1222.[10]YANG Z F, SUN T, CUI B S, et al. Environmental flow requirements for integrated water resources allocation in the Yellow River Basin, China[J]. Communications in nonlinear science & numerical simulation, 2009, 14(5):2469-2481.