Studies on prediction models of <i>Dendrolimus superans</i> occurrence area based on machine learning

ZHANG Wen-yi; JING Tian-zhong; YAN Shan-chun

doi:10.13332/j.1000-1522.20160205

Journal of Beijing Forestry University > 2017 > 39(1): 85-93. > DOI: 10.13332/j.1000-1522.20160205

ZHANG Wen-yi, JING Tian-zhong, YAN Shan-chun. Studies on prediction models of Dendrolimus superans occurrence area based on machine learning[J]. Journal of Beijing Forestry University, 2017, 39(1): 85-93. DOI: 10.13332/j.1000-1522.20160205

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Studies on prediction models of Dendrolimus superans occurrence area based on machine learning

School of Forestry, Northeast Forestry University, Harbin, Heilongjiang, 150040, P. R. China

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Received Date: June 19, 2016
Revised Date: October 22, 2016
Published Date: December 31, 2016

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Abstract

Abstract

Dendrolimus superans is one of the major forest pest insects, and its occurrence causes serious reductions in forest growth and significant threats to the safety of forest resources in China. Therefore, it is critical and necessary to predict the D. superans occurrence trend and population dynamics timely and accurately. Many factors affect the occurrence and outbreaks of pests, most likely involved in complex nonlinear systems. Unfortunately, most traditional models were based on linear prediction with very poor forecasting accuracy. In this study, following four variables, evaporation in mid March of current year, the average minimum temperature in early July of previous year, the extreme minimum temperature in late March of current year and the average wind speed in early November of previous year, were chosen as the independent variables, whereas the insect pest occurrence area was selected as the dependent variable. Three machine learning algorithms, i.e. multilayer feed-forward neural networks (MLFN), general regression neural network (GRNN), and support vector machine (SVM) were used to predict the D. superans occurrence areas, and these prediction results were compared with those predicted by the traditional multiple linear regression method. Results showed that the prediction efficacies of the machine learning methods were largely superior to multiple linear regression prediction; with the support vector machine model being the best, reaching 100% prediction accuracy with a permissible error range of 30% tolerance, and with low RMSE value (0.077) and short training time (1 second). These results suggest that machine learning algorithms, especially the support vector machine model, might have a great potential for accurate and effective predictions of insect pest occurrence areas as a reliable prediction tool.
- insect pest forecast,
- prediction model,
- multiple linear regression,
- machine learning

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[1]	王文龙.落叶松毛虫成虫种群数量监测技术研究[D].北京: 北京林业大学, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10022-1014327487.htm WANG W L. Technology of population dynamics monitoring of Dendrolimus superans (Butler) adult[D]. Beijing: Beijing Forestry University, 2014. http://cdmd.cnki.com.cn/Article/CDMD-10022-1014327487.htm
[2]	邹莉, 张国权, 黄建伟.东北地区落叶松毛虫研究进展[J].安徽农业科学, 2014, 42(9): 2584-2587. doi: 10.3969/j.issn.0517-6611.2014.09.024 ZOU L, ZHANG G Q, HUANG J W. Research progress of Dendrolimus superansis in Northeast of China[J]. Journal of Anhui Agricutural Science, 2014, 42(9): 2584-2587. doi: 10.3969/j.issn.0517-6611.2014.09.024
[3]	高宝嘉.关于森林有害生物可持续控制的思考[J].北京林业大学学报, 1999, 21(4): 112-115. doi: 10.3321/j.issn:1000-1522.1999.04.024 GAO B J. Consideration on sustainable control of forest pests[J]. Journal of Beijing Forestry University, 1999, 21(4): 112-115. doi: 10.3321/j.issn:1000-1522.1999.04.024
[4]	薛贤清.在马尾松毛虫测报中应用逐步回归电算方法的研究[J].林业科学, 1984, 20(1): 42-49. http://www.linyekexue.net/CN/Y1984/V20/I1/42 XUE X Q. A study on the application of successive regression by computerization method to the forecast of Dendrolimus punctarus[J]. Scientia Silvae Sinicae, 1984, 20(1): 42-49. http://www.linyekexue.net/CN/Y1984/V20/I1/42
[5]	梁其伟.四川省马尾松毛虫不同发生类型区的气候条件判别分析[J].数理统计与管理, 1990(1): 12-16. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK000002792926 LIANG Q W. Discriminant analysis on climatic conditions of different occurrence types of Dendrolimus punctatus in Sichuan Province[J]. Journal of Applied Statistics and Management, 1990(1): 12-16. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK000002792926
[6]	李天生, 赵良彪, 卢崇飞, 等.用双重筛选逐步回归法对广西钦州县松毛虫发生进行分析与预测[J].林业科学, 1985, 21(3): 247-252. http://www.linyekexue.net/EN/abstract/abstract365.shtml LI T S, ZHAO L B, LU C F, et al. Analysis and predicting of the pine caterpillar of Qinzhou County in Guangxi Autonomous Region by means of double selection stepwise regression method[J]. Scientia Silvae Sinicae, 1985, 21(3): 247-252. http://www.linyekexue.net/EN/abstract/abstract365.shtml
[7]	吴敬, 洪伟, 翁少容, 等.马尾松毛虫消长预报的数量化模型[J].林业科学, 1983, 19(1): 39-45. http://www.linyekexue.net/CN/Y1983/V19/I1/39 WU J, HONG W, WENG S R, et al. The quantitative model for forecasting the growth and decline of Dendrolimus punctatus[J]. Scientia Silvae Sinicae, 1983, 19(1): 39-45. http://www.linyekexue.net/CN/Y1983/V19/I1/39
[8]	叶文虎, 马小明, 李天生, 等.马尾松毛虫预测预报系统的研究[J].林业科学研究, 1990, 3(5): 427-433. http://www.lykxyj.com/ch/reader/view_abstract.aspx?file_no=19900502&flag=1 YE W H, MA X M, LI T S, et al. The population prediction of the pine caterpillar[J]. Forest Research, 1990, 3(5): 427-433. http://www.lykxyj.com/ch/reader/view_abstract.aspx?file_no=19900502&flag=1
[9]	王淑芬, 张真, 陈亮.马尾松毛虫防治决策专家系统[J].林业科学, 1992, 28(1): 31-38. http://d.old.wanfangdata.com.cn/Conference/205444 WANG S F, ZHANG Z, CHEN L. An expert system for the control of Dendrolimus punctatus[J]. Scientia Silvae Sinicae, 1992, 28(1): 31-38. http://d.old.wanfangdata.com.cn/Conference/205444
[10]	王霓虹, 缪天宇, 王阿川.基于WebGIS的森林病虫害预测预报专家系统的设计与应用[J].东北林业大学学报, 2008, 36(1): 79-80, 82. doi: 10.3969/j.issn.1000-5382.2008.01.032 WANG N H, LIAO T Y, WANG A C. Research and application of web-based forecast expert system for forest disease and insects[J]. Journal of Northeast Forestry University, 2008, 36(1): 79-80, 82. doi: 10.3969/j.issn.1000-5382.2008.01.032
[11]	高倩.落叶松毛虫预测模型与模拟研究[D].保定: 河北农业大学, 2012. http://cdmd.cnki.com.cn/Article/CDMD-11920-1012332275.htm GAO Q. The study on prediction model and simulation of Dendrolimus superans[D]. Baoding: Agricultural University of Hebei, 2012. http://cdmd.cnki.com.cn/Article/CDMD-11920-1012332275.htm
[12]	王霓虹, 李丹, 潘华.落叶松毛虫虫害防治预测咨询平台的研建[J].北京林业大学学报, 2009, 31(增刊2): 31-35. http://bjly.chinajournal.net.cn/editorc/WebPublication/paperDigest.aspx?paperID=BJLY2009S2006&isCnki=ck01 WANG N H, LI D, PAN H. Consultation platform of prevention and forecasting for Dendrolimus superans[J]. Journal of Beijing Forestry University, 2009, 31(Suppl. 2): 31-35. http://bjly.chinajournal.net.cn/editorc/WebPublication/paperDigest.aspx?paperID=BJLY2009S2006&isCnki=ck01
[13]	戚莹.基于模糊神经网络的森林虫害预测预报的应用研究[D].哈尔滨: 东北林业大学, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10225-1011146672.htm QI Y. The research on forest pest forecast based on fuzzy neural network[D]. Harbin: Northeast Forestry University, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10225-1011146672.htm
[14]	闫友彪, 陈元琰.机器学习的主要策略综述[J].计算机应用研究, 2004, 21(7): 4-10. doi: 10.3969/j.issn.1001-3695.2004.07.002 YAN Y B, CHEN Y Y. A survey on machine learning and its main strategy[J]. Application Research of Computers, 2004, 21(7): 4-10. doi: 10.3969/j.issn.1001-3695.2004.07.002
[15]	杨淑香, 赵慧颖, 包兴华.基于人工神经网络的落叶松毛虫发生量预测模型的研究[J].中国农学通报, 2014, 30(28): 72-75. http://d.old.wanfangdata.com.cn/Periodical/zgnxtb201428014 YANG S X, ZHAO H Y, BAO X H. A study on the forecast model of Dendrolimus superans butler occurrence based on artificial neural network[J]. Chinese Agricultural Science Bulletin, 2014, 30(28): 72-75. http://d.old.wanfangdata.com.cn/Periodical/zgnxtb201428014
[16]	张景阳, 潘光友.多元线性回归与BP神经网络预测模型对比与运用研究[J].昆明理工大学学报(自然科学版), 2013, 38(6): 61-67. doi: 10.3969/j.issn.1007-855x.2013.06.010 ZHANG J Y, PAN G Y. Comparison and application of multiple regression and BP neural network prediction model[J]. Journal of Kunming University of Science and Technology (Natural Science Edition), 2013, 38(6): 61-67. doi: 10.3969/j.issn.1007-855x.2013.06.010
[17]	毛健, 赵红东, 姚婧婧.人工神经网络的发展及应用[J].电子设计工程, 2011, 19(24): 62-65. doi: 10.3969/j.issn.1674-6236.2011.24.022 MAO J, ZHAO H D, YAO J J. Application and prospect of artificial neural network[J]. Electronic Design Engineering, 2011, 19(24): 62-65. doi: 10.3969/j.issn.1674-6236.2011.24.022
[18]	HOPFIELD J J. Artificial neural networks[J]. IEEE Circuit & Devices Magazine, 1988, 4(5): 3-10. http://d.old.wanfangdata.com.cn/Periodical/gpxygpfx201509047
[19]	YEGNANARAYANA B. Artificial neural networks[M]. New Delhi: PHI Learning Pvt. Ltd., 2009.
[20]	DAYHOFF J E, DELEO J M. Artificial neural networks[J]. Cancer, 2001, 91(Suppl. 8): 1615-1635. http://d.old.wanfangdata.com.cn/Periodical/gpxygpfx201509047
[21]	SPECHT D F. A general regression neural network[J]. IEEE Transactions on Neural Networks, 1991, 2(6): 568-576. doi: 10.1109/72.97934
[22]	田万银, 徐华潮.基于相空间重构及GRNN的海防林害虫预测及效果检验[J].浙江林业科技, 2014, 34(2): 65-69. doi: 10.3969/j.issn.1001-3776.2014.02.016 TIAN W Y, XU H C. Coastal protection forest pest forecasting and verification based on phase space reconstruction and GRNN[J]. Journal of Zhejiang Forestry Science and Technology, 2014, 34(2): 65-69. doi: 10.3969/j.issn.1001-3776.2014.02.016
[23]	金帅军.基于GRNN神经网络的农作物虫害量预测系统设计[D].呼和浩特: 内蒙古工业大学, 2013. http://cdmd.cnki.com.cn/Article/CDMD-10128-1014012754.htm JIN S J. Design the prediction of system insect pests of crop based on GRNN neural network[D]. Hohhot: Inner Mongolia University of Technology, 2013. http://cdmd.cnki.com.cn/Article/CDMD-10128-1014012754.htm
[24]	SVOZIL D, KVASNICKAB V, POSPICHALB J. Introduction to multi-layer feed-forward neural networks[J]. Chemometrics and Intelligent Laboratory Systems, 1997, 39(1): 43-62. doi: 10.1016/S0169-7439(97)00061-0
[25]	JOHANSSON E M, DOWLA F U, GOODMAN D M. Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method[J]. International Journal of Neural Systems, 1991, 2(4): 291-301. doi: 10.1142/S0129065791000261
[26]	SPECHT D F. A general regression neural network[J]. IEEE Transactions on Neural Networks, 1991, 2(6): 568-576. doi: 10.1109/72.97934
[27]	GOULERMA J Y, LIATSIS P, ZENG X J, et al. Density-driven generalized regression neural networks (DD-GRNN) for function approximation[J]. IEEE Transactions on Neural Networks, 2007, 18(6): 1683-1696. doi: 10.1109/TNN.2007.902730
[28]	KHAN J, WEI J S, RINGNE M, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks[J]. Nature Medicine, 2001, 7(6): 673-679. doi: 10.1038/89044
[29]	琚存勇, 蔡体久.鄂尔多斯草地生物量估测的GRNN模型实现[J].北京林业大学学报, 2008, 30(增刊1): 296-299. http://j.bjfu.edu.cn/article/id/8516 JU C Y, CAI T J. Estimation of aboveground biomass using GRNN model in Ordos grassland[J]. Journal of Beijing Forestry University, 2008, 30(Suppl. 1): 296-299. http://j.bjfu.edu.cn/article/id/8516
[30]	CORNE S A, CARVER S J, KUNIN W E, et al. Predicting forest attributes in southeast Alaska using artificial neural networks[J]. Forest Science, 2004, 50(2): 259-276.
[31]	KANDIEMAZ H M, KABA K, AVCI M. Estimation of monthly sunshine duration in turkey using artificial neural networks[J/OL]. International Journal of Photoenergy, 2014(2): 1-9[2016-05-10]. http://dx.doi.org/10.1155/2014/680596.
[32]	SCHOLKOPF B, SUNG K K, BURGES C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers[J]. IEEE Transactions on Signal Processing, 1997, 45(11): 2758-2765. doi: 10.1109/78.650102
[33]	DENG N Y, TIAN Y J, ZHANG C H. Support vector machines: optimization based theory, algorithms, and extensions[M]. 1st ed. New York: Chapman & Hall/CRC, 2012.
[34]	ZHONG X M, LI J P, DOU H C, et al. Fuzzy nonlinear proximal support vector machine for land extraction based on remote sensing image[J/OL]. PloS ONE, 2013, 8(7): e69434[2016-05-06]. DOI: 10.1371/journal.pone.0069434.
[35]	SHEN Y, HE Z, WANG Q, et al. Feature generation of hyperspectral images for fuzzy support vector machine classification[C]. Graz: IEEE Instrumentation and Measurement Technology Conference, 2012: 1977-1982.
[36]	林卓, 吴承祯, 洪伟, 等.基于BP神经网络和支持向量机的杉木人工林收获模型研究[J].北京林业大学学报, 2015, 37(1): 42-47. doi: 10.13332/j.cnki.jbfu.2015.01.008 LIN Z, WU C Z, HONG W, et al. Yield model of Cunninghamia lanceolata plantation based on back propagation neural network and support vector machine[J]. Journal of Beijing Forestry University, 2015, 37(1): 42-47. doi: 10.13332/j.cnki.jbfu.2015.01.008
[37]	YANG D, LI H, CHEN F, et al. A novel qualitative proof approach of the Dulong-Petit law using general regression neural networks[C]. Ottwa: 2014 IEEE Electronics, Computer and Applications, 2014: 577-580.
[38]	罗瑜.支持向量机在机器学习中的应用研究[D].成都: 西南交通大学, 2007. http://cdmd.cnki.com.cn/article/cdmd-10613-2008041983.htm LUO Y. Study on application of machine learning based on support vector machine[D]. Chengdu: Southwest Jiaotong University, 2007. http://cdmd.cnki.com.cn/article/cdmd-10613-2008041983.htm
[39]	费海泽, 王鸿斌, 孔祥波, 等.马尾松毛虫发生相关气象因子筛选及预测[J].东北林业大学学报, 2014, 42(1): 136-140. doi: 10.3969/j.issn.1000-5382.2014.01.030 FEI H Z, WANG H B, KONG X B, et al. Selection and prediction of meteorological factors correlated with Dendrolimus punctatus outbreak[J]. Journal of Northeast Forestry University, 2014, 42(1): 136-140. doi: 10.3969/j.issn.1000-5382.2014.01.030
[40]	陈梅香.基于SVM和GIS的梨小食心虫预测系统的研究[D].北京: 北京林业大学, 2010. http://cdmd.cnki.com.cn/Article/CDMD-10022-2010031389.htm CHEN M X. Studies on prediction system of Grapholitha molesta(Busck) based on support vector machine and geographic information system[D]. Beijing: Beijing Forestry University, 2010. http://cdmd.cnki.com.cn/Article/CDMD-10022-2010031389.htm
[41]	向昌盛, 周子英, 张林峰.支持向量机在害虫发生量预测中的应用[J].生物信息学, 2011, 9(1): 28-31. doi: 10.3969/j.issn.1672-5565.2011.01.007 XIANG C S, ZHOU Z Y, ZHANG L F. Application of pest occurrence prediction based on support vector machine[J]. China Journal of Bioinformatics, 2011, 9(1): 28-31. doi: 10.3969/j.issn.1672-5565.2011.01.007

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Studies on prediction models of Dendrolimus superans occurrence area based on machine learning