基于PSO的DSSAT水稻品种参数优化  

Rice Cultivar Coefficient Optimization of DSSAT Based on PSO

作　　者：王斌 杨宏贤 冯杰 郭中原 郭彦文 WANG Bin;YANG Hongxian;FENG Jie;GUO Zhongyuan;GUO Yanwen(College of Water Conservancy and Architecture,Northeast Agricultural University,Harbin 150030,China;Huaian Surveying and Design Institute of Water Resources Co.,Ltd.,Huaian 223001,China;Heilongjiang Institute of Water Resources Research,Harbin 150080,China;Qing’an County Northern Oasis Rice Research Institute,Qing’an 152400,China)

机构地区：[1]东北农业大学水利与土木工程学院,哈尔滨150030 [2]淮安市水利勘测设计研究院有限公司,淮安223001 [3]黑龙江省水利科学研究院,哈尔滨150080 [4]庆安县北方绿洲稻作研究所,庆安152400

出　　处：《农业机械学报》2023年第11期369-375,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金：黑龙江省自然科学基金项目(LH2021E009)。

摘　　要：农业技术转移决策支持系统(DSSAT)在农业领域的应用越来越广泛,应用DSSAT的首要工作就是估计作物品种参数。GLUE参数估计器是DSSAT自带的参数估计工具,但GLUE参数估计器所估计的品种参数并不总有效,其估计参数的DSSAT模拟精度往往不高。本文利用4个品种水稻的田间实测产量数据,采用对比分析方法,以DSSAT自带的GLUE参数估计器运行结果为参照,将粒子群优化(PSO)的每个粒子视为一组水稻品种参数,在运行PSO算法过程中调用DSSAT模拟水稻产量,依据产量模拟误差和PSO的运行机制修改粒子,从而验证PSO优化DSSAT水稻品种参数的有效性及可行性。研究结果表明:两种算法均能较好识别DSSAT水稻品种参数,但GLUE参数估计器估计参数无效的频次较高;与GLUE参数估计器相比,PSO识别的参数均为有效参数,其优化参数的DSSAT模拟水稻产量的精度更高,标准化均方根误差(NRMSE)处于5.98%~8.78%之间,明显低于GLUE参数估计器的6.89%~18.06%,所模拟的水稻产量也更接近于实测产量。Decision support system for agrotechnology transfer(DSSAT)is increasingly used in agriculture,and the primary task in the localization of DSSAT is to estimate crop cultivar coefficients.Generalized likelihood uncertainty estimation(GLUE)coefficient estimator is a self⁃contained coefficient estimation tool for DSSAT,but the crop cultivar coefficients estimated by GLUE coefficient estimator are not always effective,and the simulation accuracy of the DSSAT with the estimated coefficents is often not high.Through using the field measured yield data of four cultivars of rice and the comparative analysis method,with the results of running the GLUE coefficient estimator as a reference,treating each particle of particle swarm optimization(PSO)was considered as a group of rice cultivar coefficients,calling DSSAT to simulate rice yield during the operation of the PSO,and modifying the particles according to the yield simulation error and the operation mechanism of PSO,thus verifying the feasibility of PSO to optimize the coefficients of DSSAT rice cultivar coefficients.The results showed that both algorithms can identify the DSSAT rice cultivar coefficients well,but the GLUE coefficient estimator had a higher frequency of estimating invalid coefficients.Compared with the GLUE coefficient estimator,the coefficients identified by the PSO were all efficient,and the accuracy of its optimized parameters for DSSAT simulated rice yield was higher,and the normalized root mean square error(NRMSE)was in the range of 5.98%~8.78%,which was significantly lower than that of the GLUE coefficient estimator,which was ranged from 6.89%to 18.06%,and the simulated rice yield was close to the measured yield.

关 键 词：水稻品种参数 DSSAT PSO GLUE参数估计器 

分 类 号：S115[农业科学—农业基础科学]