基于气象因子的森林病虫鼠害发生率预测模型研究

林业资源管理 ›› 2012, Vol. 0 ›› Issue (4): 96-101.

基于气象因子的森林病虫鼠害发生率预测模型研究

张乃静, 鞠洪波, 纪平

中国林业科学研究院资源信息研究所,北京 100091

收稿日期:2012-05-03 修回日期:2012-07-15 出版日期:2012-08-28 发布日期:2020-11-27
通讯作者: 纪平(1964-),女,天津人,副研究员,主要从事数据库技术与应用研究工作。
作者简介:张乃静(1982-),女,天津人,在读博士,主要从事数据库技术与应用研究工作。Email:naijing.zhang@gmail.com
基金资助:
国家科技基础条件平台建设项目(2005DKA32200);Tropical Forest Fire Monitoring and Management System Based on Satellite Remote Sensing Data in China [ITTO PD 228/03 Rev.3(F)]

Prediction of Incidence Rate of Forest Diseases, Mice and Pests Using Meteorological Factor

ZHANG Naijing, JU Hongbo, JI Ping

Research Institute of Forestry Information Techniques,Chinese Academy of Forestry,Beijing 100091,China

Received:2012-05-03 Revised:2012-07-15 Online:2012-08-28 Published:2020-11-27

摘要/Abstract

摘要： 基于气象因子,使用一元线性回归、多元线性回归、一元非线性回归以及BP神经网络4种不同的回归模型对森林病虫鼠害发生率进行预测,结果表明:对于线性模型,多元线性回归模型的判定系数和均方根误差均优于一元线性回归模型;对于非线性模型,BP神经网络模型的判定系数和均方根误差均优于一元非线性回归模型;按优劣排序为BP神经网络模型、一元非线性回归模型、多元线性回归模型和一元线性回归模型。气象因子与森林病虫鼠害发生率的关系并非单纯的线性关系,非线性的预测模型可以更好地解释森林病虫鼠害的发生程度。

关键词: 气象因子, 森林病虫鼠害, 预测模型

Abstract: Based on some meteorological factors, prediction was made on the incidence rate of forest diseases,mice and pests by simple linear regression(SLR),multiple linear regressions(MLR),simple non-linear regression(SNLR)and BP neural network(BP).The results show that the determination coefficient(R²)and root mean square error(RMSE)of MLR are better than that of SLR,the R² and RMSE of BP are better than that of SNLR.According to the quality of models,BP model is the best of all,and SNLR is better than MLR and SLR.The conclusion is that the relations between meteorological factors and incidence rate of forest diseases,mice and pests are not linearity,non-linear model could effectively explain the incidence rate of forest diseases,mice and pests.

Key words: meteorological factor, forest diseases, mice and pests, prediction model

中图分类号:

S443
S764.5

张乃静, 鞠洪波, 纪平. 基于气象因子的森林病虫鼠害发生率预测模型研究[J]. 林业资源管理, 2012, 0(4): 96-101.

ZHANG Naijing, JU Hongbo, JI Ping. Prediction of Incidence Rate of Forest Diseases, Mice and Pests Using Meteorological Factor[J]. FOREST RESOURCES WANAGEMENT, 2012, 0(4): 96-101.

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