DOI: 10.3724/SP.J.1006.2018.01229

Acta Agronomica Sinica (作物学报) 2018/44:8 PP.1229-1236

Parameter Optimization in APSIM-Based Simulation Model for Yield For-mation of Dryland Wheat Using Shuffled Frog Leaping Algorithm

The rapid and accurate estimation of model parameters is an important prerequisite for the application of yield formation model. In the process of localization parameters calibration for yield formation based on APSIM (agricultural production systems simulator) model of dryland wheat, there are some deficiencies such as large scale, long time consuming, a lack of precision and low efficiency. In this study, intelligent algorithm was used to remedy the deficiencies. We collected and analyzed the field experimental data in Mazichuan village, Lijiabao town, Anding district, Dingxi city from 2002 to 2005, and Anjiagou village, Fengxiang town, Anding district, Dingxi city from 2015 to 2016, and the historical and meteorological data in Anding district, Dingxi city from 1971 to 2016. According to the characteristics of the yield formation model for parameters nonlinearity and multidimensional change, making full use of the intelligent strategy of advanced group rotation and global information exchange in shuffled frog leaping algorithm and the self-organization, self-learning intelligent algorithm characteristics, the estimation parameters more difficult to obtain in the model of the dryland wheat yield formation based on APSIM platform were optimized and tested by correlation analysis method. This optimization method could use frog intelligent group biology evolution learning strategy to estimate the yield formation model parameters of dryland wheat. Compared with the method of attempting to eliminate the error, which is used in the localization parameters calibration of APSIM platform usually, the accuracy of simulation output was significantly improved. The root mean square error (RMSE) reduced from 79.13 kg ha-1 to 35.36 kg ha-1, the normalized root mean square error (NRMSE) decreased from 5.97% to 2.63%, and the model effectiveness index (ME) increased from 0.939 to 0.989. This method has strong global optimization ability, reasonable calculation quantity, and fast convergence speed.

Key words:Wheat,Shuffled frog leaping algorithm,APSIM,Parameters optimization

ReleaseDate:2019-11-05 15:29:17

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