基于Bayesian-LightGBM模型的粮食产量预测研究

Research on grain yield prediction based on Bayesian-LightGBM model

目前用于粮食产量预测模型如灰色关联模型普遍存在训练速度较慢、预测精度较低等问题。为解决该问题,以轻量级梯度提升机(LightGBM)模型为基础,将其损失函数修正为Huber损失函数,同时引入贝叶斯优化算法确定出最优超参数组合并输入该模型。以广西的早、晚水稻产量及16个粮食产量影响因素为数据集进行仿真试验,结果表明:基于线性回归的预测模型的平均绝对值误差为1.255,基于决策树的预测模型的平均绝对值误差为0.426,基于随机森林的预测模型的平均值误差为0.315,基于Bayesian-LightGBM的预测模型的平均绝对值误差为0.049。相比其他预测模型,Bayesian-LightGBM粮食产量预测模型能够更有效地实现粮食产量预测,预测精度更高。

At present,the grain yield prediction models,such as the grey relational model,generally have problems such as slow training speed and low prediction accuracy.In order to solve the above problems,this paper is based on the Lightweight Gradient Boosting Machine(LightGBM)model,and its loss function is modified to a Huber loss function,and a Bayesian optimization algorithm is introduced to determine the optimal hyperparameter combination and input into the model.Simulation experiments were carried out on the data sets of early and late rice yields and 16 grain yield influencing factors in Guangxi.The results showed that the average absolute error of the prediction model based on linear regression was 1.255,the average absolute error of the prediction model based on decision tree was 0.426,the average absolute error of the prediction model based on random forest was 0.315,and the average absolute error of the prediction model based on Bayesian LightGBM was 0.049.Compared with other prediction models,Bayesian LightGBM grain yield prediction model can realize grain yield prediction more effectively,with higher prediction accuracy.