Abstract：In order to improve the prediction accuracy of modelling reference crop evapotranspiration, the bat algorithm was used to optimize extreme learning machine(ELM). Meteorological data from 1966 to 2015 at Shantou Station(i.e., monthly maximum and minimum ambient temperatures, global solar radiation, wind speed and relative humidity)was used to train and test the proposed models of extreme learning machine. The bat algorithm was used to optimize the regularization coefficient and breadth of radial basis function of ELM with a cross-verification method. Finally, the performance of proposed models for the reference crop evapotranspiration estimation was evaluated by statistical indicators. The results show that the bat algorithm-based optimized ELM model provides better accurate and stable values of reference crop evapotranspiration in comparison with the evapotranspiration values estimated by the models optimized with traditional tuning method and genetic algorithm, respectively.
吴立峰, 鲁向晖*, 刘小强, 张苏扬, 刘明美, 董建华. 蝙蝠算法优化极限学习机模拟参考作物蒸散量[J]. 排灌机械工程学报, 2018, 36(9): 802-805.
WU Lifeng, LU Xianghui*, LIU Xiaoqiang, ZHANG Suyang, LIU Mingmei, DONG Jianhua. Simulation of reference crop evapotranspiration by using bat algorithm optimization based extreme learning machine. Journal of Drainage and Irrigation Machinery Engin, 2018, 36(9): 802-805.
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