摘要
现存农作物单产预测方法往往是在历史数据完备的前提下进行精确数值预测,未能真实体现农作物单产系统的不确作定性,而且无法对既成现实进行诊断分析。在决策需求仅仅要求产量等级水平的假设下,本文把不确定性信息处理方法 Credal网络模型引入到农作物单产预测系统,通过分析农作物单产系统各影响因素之间的关系,提出类要素和影响因子的概念,构建了进行农作物单产预测的通用Credal网络模型。把农作物单产量的等级水平状态作为概率事件,通过Credal网络前向推理功能预测其状态发生的概率,把高概率事件发生的等级状态作为其单产预测等级,实现了在已知部分事实发生情况下的知识推理。此外,利用Credal网络的后向推理功能,实现对农作物低产事实下的诊断分析,找出影响低产的关键类要素和影响因子。最后结合算例,采用基于扩展关系模型的近似推理算法阐述了利用该模型对农作物单产进行预测和诊断的应用过程,推理结果合理,表明该方法是可行的,且具有可释性,为农业生产科学决策提供了方法指导。
Existing crop yield prediction methods,often driven by huge historical data for precise estimation,hardly reflect the complexities and uncertainties of crop yield system.Thus,such models could not be used to predict crop yield by using scanty data or to adequately determine phenological events.Assuming that only known yield levels are need for decision-making,Credal network model（which better processes uncertainties） was introduced for predicting crop yield systems.This paper predicted yield levels and determined low yield indicators with scanty data.The paper analyzed the relationship between crop yield and affecting factors and proposed the concept of class element.Also an impact factor concept was proposed to describe the importance of the elements.Fur-thermore,a general crop yield prediction model based on Credal network was constructed,illustrating the relationships among crop class elements and impact factors.In the constructed prediction Credal network model,every node was assigned with many states treated as probability events.Every state was connected with a conditional or marginal probability representing relationships of vari-ables called parameters.Parameters were denoted as forms of Credal sets to describe system uncertainty.By using forward inference capabilities of Credal network along with ＂crop yield＂ variable as objective function,states probabilities of occurrence were calcu-lated with incomplete evidence.Then probabilities of state events higher than others were used in yield forecasts.The process reck-oned with knowledge reasoning in the face of scanty facts.By further using backward inference function of Credal network,diagno-sis of low-yield facts was accomplished with incomplete evidence.It was noted that the key impact factors and class elements trig-gered low yield.Combined with a scenario of climate and production as the main factors,interaction was demonstrated with ap-proximation inference algorithm based on extended relational database model.The model illustrated how forecasting and diagnosis based on Credal network could be carried out for crop yield.The inference result showed that the method was feasible,reasonable and interpretable.It fully demonstrated the model functions such as forecasting,interpreting and diagnosis in crop yield systems.The crop yield prediction model based on Credal network enriched existing methodology for crop yield prediction.If in addition to cli-mate and production factors,bio-agronomic,economic measurement and plant growth factors were taken into account,the model provided an all-around method with accurate yield forecasting.The model also supported scientific decision-making in agricultural productions to avoid detrimental factor effects.
出处
《中国生态农业学报》
CAS
CSCD
北大核心
2012年第6期782-787,共6页
Chinese Journal of Eco-Agriculture
基金
国家自然科学基金项目(71071049)
河北省自然基金项目(G2011208087)资助
