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作物学报 ›› 2018, Vol. 44 ›› Issue (8): 1229-1236.doi: 10.3724/SP.J.1006.2018.01229

• 耕作栽培·生理生化 • 上一篇    下一篇

利用混合蛙跳算法优化基于APSIM的旱地小麦产量形成模型参数

聂志刚1,2(),李广3,*(),雒翠萍2,马维伟3,代永强2   

  1. 1 甘肃农业大学资源与环境学院, 甘肃兰州 730070
    2 甘肃农业大学信息科学技术学院, 甘肃兰州 730070
    3 甘肃农业大学林学院, 甘肃兰州 730070
  • 收稿日期:2017-07-27 接受日期:2018-03-26 出版日期:2018-08-10 网络出版日期:2018-04-24
  • 通讯作者: 李广
  • 基金资助:
    国家自然科学基金项目(31660348);国家自然科学基金项目(31560378);国家自然科学基金项目(31560343);甘肃农业大学青年导师基金项目(GAU-QNDS-201701)

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

Zhi-Gang NIE1,2(),Guang LI3,*(),Cui-Ping LUO2,Wei-Wei MA3,Yong-Qiang DAI2   

  1. 1 College of Resource and Environment Science, Gansu Agricultural University, Lanzhou 730070, Gansu, China
    2 College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, Gansu, China
    3 College of Forestry, Gansu Agricultural University, Lanzhou 730070, Gansu, China
  • Received:2017-07-27 Accepted:2018-03-26 Published:2018-08-10 Published online:2018-04-24
  • Contact: Guang LI
  • Supported by:
    the National Natural Science Foundation of China(31660348);the National Natural Science Foundation of China(31560378);the National Natural Science Foundation of China(31560343);the Youth Tutor Foundation of Gansu Agricultural University(GAU-QNDS-201701)

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摘要：

模型参数的快速、准确估算是产量形成模型应用的重要前提。在基于APSIM (agricultural production systems simulator)的旱地小麦产量形成模型参数本土化率定过程中, 存在体量大、耗时长、精度低、效率低的缺点, 本研究利用智能算法优化模型参数, 试图解决上述问题。依据甘肃省定西市安定区李家堡镇麻子川村2002—2005年、凤翔镇安家沟村2015—2016年大田试验数据以及定西市安定区1971—2016年气象和产量资料, 运用混合蛙跳算法分组轮换和全局信息交换的智能策略, 对基于APSIM的旱地小麦产量形成模型参数进行了优化, 并采用相关性分析方法检验。该优化方法利用青蛙智能的群体生物进化学习策略, 可实现对小麦产量形成模型参数的估算, 较APSIM平台参数本土化率定常用的穷举试错法, 参数优化后产量模拟精度显著提高, 均方根误差(RMSE)平均值由79.13 kg hm -2降低到35.36 kg hm -2, 归一化均方根误差(NRMSE)平均值由5.97%降低到2.63%, 模型有效性指数(ME)平均值由0.939提高到0.989。该方法全局优化能力强, 收敛速度快。

关键词: 小麦, 混合蛙跳算法, APSIM, 参数优化

Abstract:

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

表1

APSIM平台中研究区主要土壤属性参数和小麦有效水分下限"

项目
Item		 土层深度 Soil depth
5 cm 10 cm 30 cm 50 cm 80 cm 110 cm 140 cm 170 cm 200 cm
容重 BD (g cm-3) 1.29 1.23 1.32 1.20 1.14 1.14 1.13 1.12 1.11
萎蔫系数 WC (mm mm-1) 0.08 0.08 0.08 0.08 0.09 0.09 0.11 0.13 0.13
最大持水量 DUL (mm mm-1) 0.27 0.27 0.27 0.27 0.26 0.27 0.26 0.26 0.26
饱和水分含量 SM (mm mm-1) 0.46 0.49 0.45 0.50 0.52 0.52 0.48 0.53 0.53
风干系数 CA (mm mm-1) 0.01 0.01 0.05 0.07 0.07 0.07 0.07 0.07 0.07
土壤导水率 SWC (mm h-1) 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.60
小麦有效水分下限 LLW (mm mm-1) 0.09 0.09 0.09 0.09 0.09 0.10 0.11 0.13 0.15

图1

混合蛙跳算法对APSIM小麦产量形成模型参数的优化过程"

表2

小麦产量形成模型参数优化比较"

参数
Parameter		 默认值
Default value		 优化值
Optimized value
每克茎籽粒数
Grain number per gram stem (grain g-1)		 25 26
开花到灌浆开始日潜在籽粒平均灌浆速率
Daily potential rate of grain filling from flowering to start of grain filling (g grain-1)		 0.00100 0.00112
灌浆期日潜在籽粒平均灌浆速率
Daily potential rate of grain filling during grain filling (g grain-1)		 0.00200 0.00249
日潜在籽粒平均氮积累速率
Daily potential rate of nitrogen accumulation (g grain-1)		 0.000055 0.000067
日籽粒氮积累速率下限
Minimum rate of nitrogen accumulation (g grain-1)		 0.000015 0.000018
单株最大籽粒干重
Maximum grain dry weight per plant (g)		 0.0400 0.0437

图2

旱地小麦产量模拟值与实测值间的关系"

表3

参数优化前后小麦产量形成模型模拟检验结果"

模型参数
Model parameter		 麻子川村 Mazichuan 安家沟村 Anjiagou
RMSE (kg hm-2) NRMSE (%) ME RMSE (kg hm-2) NRMSE (%) ME
默认值 Default value 64.21 4.33 0.966 94.05 7.61 0.912
优化值 Optimized value 33.64 2.27 0.991 36.88 2.99 0.986
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