基于机器学习的辽宁省粮食产量预测模型分析  

Analysis of Grain Yield Prediction Model in Liaoning Province Based on Machine Learning

作　　者：安紫涵 AN Zihan(Dalian University of Foreign Languages,Dalian Liaoning 116044,China)

机构地区：[1]大连外国语大学,辽宁大连116044

出　　处：《信息与电脑》2024年第18期197-199,共3页Information & Computer

摘　　要：本研究对辽宁省粮食产量的相关数据进行了收集与整理,通过皮尔逊相关性分析确定了影响辽宁省粮食产量的主要因素,并利用各影响因素与辽宁省粮食产量关系图,在选择模型时优先考虑了非线性模型。具体而言,本研究采用了多层感知机(Multi-Layer Perceptron,MLP)、随机森林和XGBoost模型,并通过平均绝对误差(Mean Absolute Error,MAE)、均方根误差(Root Mean Squared Error,RMSE)和R方值(R-Squared)对比各模型的预测准确性。结果表明,多层感知机模型有更高的预测准确性,能够更精准地预测粮食产量。In this study,the relevant data of grain production in Liaoning Province are collected and organized,and the main factors affecting grain production in Liaoning Province are identified through Pearson correlation analysis,and the relationship graph between each influencing factor and grain production in Liaoning Province is utilized,and the nonlinear model is prioritized in the selection of the model.Specifically,multi-layer perceptron(MLP),random forest,and XGBoost models are used in this study,and the prediction accuracy of each model is compared by mean absolute error(MAE),root mean squared error(RMSE)and R-Squared.The results show that the multilayer perceptron model has higher prediction accuracy and can predict grain yield more accurately.

关 键 词：机器学习 粮食产量预测 多层感知机 

分 类 号：S126[农业科学—农业基础科学]