基于遗传算法的森林抚育间伐小班智能选择
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摘要
【目的】探索在森林抚育间伐任务目标控制下,基于空间分析和遗传算法的森林抚育间伐小班智能选择方法,为以小班为单位的作业方案编制等后续森林经营活动提供决策支持。【方法】以内蒙古赤峰市桦木沟林场为例,根据抚育间伐任务和经营者指定的基本属性条件,基于空间查询或点缓冲区分析方式选择出空间分布连续的小班作为初始小班集合,设计环带控制算法(ACA)动态计算点缓冲区的初始半径和步长。以迫切度、难易度和立地因子构建目标条件函数,用目标条件值衡量小班与任务目标的符合程度,并以目标条件值最大为目标条件、任务面积为限制条件建立抚育间伐小班智能选择的数学模型,使用改进的遗传算法(IGSEGA)进行求解,从而对初始小班集合内的小班进行优选,得到最符合任务目标的小班集合。【结果】研究区内指定任务面积300 hm~2、上限值5%和其他条件,基因交叉率0.6,变异率0.3,变长系数3,迭代次数100,目标函数各参数根据任务要求进行设置。ACA算法计算得到初始半径1 407 m,且只需1次半径扩增即可构造出初始小班集合,普通点缓冲区分析由于初始半径和步长的不确定性,分析效率明显低于ACA算法。以林场场部为中心点选择时得到小班40个,所选小班皆为符合基本条件且最接近目标条件值的小班,另外2个测试点得到的结果同样说明算法的智能性和有效性。由于IGSEGA算法构建的初始个体适应值已较为接近最优解,经14~15次迭代即可求解出任务目标小班集合,求解效率优于标准遗传算法(SGA)。【结论】提出森林抚育间伐小班智能选择概念,以迫切度、难易度和立地因子构建目标条件函数,同时构建小班选择的数学模型并采用IGSEGA算法进行求解。设计ACA算法优化点缓冲区分析,可提高空间分析效率,改进贪婪策略对遗传算法的编码方式及对应的遗传算子,为森林抚育间伐小班智能选择的实现提供一种有效方法,该方法还可应用于造林、林分改造等其他经营任务中对小班进行智能选择,为后续森林经营活动提供决策支持。
【Objective】 This study investigated the intelligent selection method of subcompartments based on spatial analysis and genetic algorithm (SGA) in order to provide decision support for formulating forest management plan,conducting under the thinning target control.【Method 】 Huamugou forest farm,in Chifeng City,Inner Mongolia,was selected as research area to simulate intelligent selection.According to the basic condition of thinning target and operator,the initial small class collection was chosen from continuous distribution of tiny space by spatial query or point buffer analysis.Initial radius and step of point buffer analysis were calculated dynamically by annulus control algorithm (ACA).Urgency indicator,difficulty indicator and site indicator constituted the objective condition formula (OCF),whose value measured the coincidence level of task object.The mathematical model was built by maximum value of OCF and task area.The solution could be obtained by improved genetic algorithm (IGSEGA),which selected the best subcompartments from the initial small class collection,and obtained the most optimal small class collection.【Result】 The parameters of OCF were set with task requirement.In research area,the task area was 300 hm~2,upper limit as 5% and other conditions.The parameters of GA were as following: gene crossover probability as 0.6,gene variation rate as 0.3,gene variable-length coefficient as 3,iterations as 100.The initial radius as 1 407 m was acquired by ACA,and the radius of expansion wasonly one time to construct the initial small class collection.Analytical efficiency of general point buffer was lower than ACA because of the uncertainty of initial radius and steps.The initial subcompartment collection could be generated through 14 to 15 iterations because the initial adaptive value was close to the optimal solution by IGSEGA,and the efficiency of solving was higher than the ordinary SGA.The center point of forestry station,40 subcompartments were obtained and conformed to the objective value.This experiment results showed that the IGSEGA is intelligent and effective.【Conclusion 】 This paper proposed a concept of forest thinning subcompartment intelligent selection,and constructed the OCF with urgency indicator,difficulty indicator and site indicator.The mathematical model of subcompartment selection was constructed and solved by IGSEGA.Analytical efficiency of buffer analysis was greatly improved by ACA.The research designed a new genetic algorithm encoding with greedy strategy and its genetic operator.It provided an effective method and technology for the concept of forest thinning subcompartment intelligent selection,and decision support for the follow-up forest management activities.
【Objective】 This study investigated the intelligent selection method of subcompartments based on spatial analysis and genetic algorithm (SGA) in order to provide decision support for formulating forest management plan,conducting under the thinning target control.【Method 】 Huamugou forest farm,in Chifeng City,Inner Mongolia,was selected as research area to simulate intelligent selection.According to the basic condition of thinning target and operator,the initial small class collection was chosen from continuous distribution of tiny space by spatial query or point buffer analysis.Initial radius and step of point buffer analysis were calculated dynamically by annulus control algorithm (ACA).Urgency indicator,difficulty indicator and site indicator constituted the objective condition formula (OCF),whose value measured the coincidence level of task object.The mathematical model was built by maximum value of OCF and task area.The solution could be obtained by improved genetic algorithm (IGSEGA),which selected the best subcompartments from the initial small class collection,and obtained the most optimal small class collection.【Result】 The parameters of OCF were set with task requirement.In research area,the task area was 300 hm~2,upper limit as 5% and other conditions.The parameters of GA were as following: gene crossover probability as 0.6,gene variation rate as 0.3,gene variable-length coefficient as 3,iterations as 100.The initial radius as 1 407 m was acquired by ACA,and the radius of expansion wasonly one time to construct the initial small class collection.Analytical efficiency of general point buffer was lower than ACA because of the uncertainty of initial radius and steps.The initial subcompartment collection could be generated through 14 to 15 iterations because the initial adaptive value was close to the optimal solution by IGSEGA,and the efficiency of solving was higher than the ordinary SGA.The center point of forestry station,40 subcompartments were obtained and conformed to the objective value.This experiment results showed that the IGSEGA is intelligent and effective.【Conclusion 】 This paper proposed a concept of forest thinning subcompartment intelligent selection,and constructed the OCF with urgency indicator,difficulty indicator and site indicator.The mathematical model of subcompartment selection was constructed and solved by IGSEGA.Analytical efficiency of buffer analysis was greatly improved by ACA.The research designed a new genetic algorithm encoding with greedy strategy and its genetic operator.It provided an effective method and technology for the concept of forest thinning subcompartment intelligent selection,and decision support for the follow-up forest management activities.
引文
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韩焱云,吴保国,刘建成,等.2014.不确定性推理在森林培育专家系统中的应用.北京林业大学学报,36(1):88-93.(Han Y Y,Wu B G,Liu J C,et al.2014.Application of uncertainty inference in the forest cultivation expert system.Journal of Beijing Forestry University,36(1):88-93.[in Chinese])
亢新刚.2011.森林经理学.北京:中国林业出版社.(Kang X G.2011.Forest management.Beijing:China Forestry Publishing House.[in Chinese])
林晗,洪滔,陈辉,等.2010.应用遗传算法的工业原料林多树种造林设计.林业科学,46(5):92-101.(Lin H,Hong T,Chen H,et al.2010.Multi-species design in planting industrial forests by genetic algorithm.Scientia Silvae Sinicae,46(5):92-101.[in Chinese])
邱荣祖,林雅惠,钟聪儿.2010.基于Arc GIS的木材物流中心选址.林业科学,46(6):113-117.(Qiu R Z,Lin Y H,Zhong C E.2010.Location of timber logistics centers based on Arc GIS.Scintia Silvae Sinicae,46(6):113-117.[in Chinese])
沈立辉,吴保国,杨乃.2014.面状要素主骨架线自动提取算法研究.武汉大学学报:信息科学版,39(7):767-771.(Shen L H,Wu B G,Yang N.2014.Areal feature main skeleton extraction algorithm.Geomatics and Information Science of Wuhan University,39(7):767-771.[in Chinese])
沈立辉.2015.森林资源经营管理平台与经营决策服务子系统的研建.北京:北京林业大学硕士学位论文.(Shen L H.2015.The research and development of forest resources management platform and decision service subsystem.Beijing:MSthesis of Beijing Forestry University.[in Chinese])
温阳,高桂英,杨跃文.2011.内蒙古主要造林树种立地质量评价研究.呼和浩特:内蒙古大学出版社.(Wen Y,Gao G Y,Yang Y W.2011.Study on site quality evaluation of main afforestation tree species in Inner Mongolia.Hohhot:Inner Mongolia University Press.[in Chinese])
吴保国,李成赞,马驰,等.2009a.森林培育专家决策支持系统的研究.北京林业大学学报,31(S2):1-8.(Wu B G,Li C Z,Ma C,et al.2009a.An expert decision support system for silviculture.Journal of Beijing Forestry University,31(S2):1-8.[in Chinese])
吴保国,马驰.2009b.森林培育专家系统中造林决策知识库设计与推理机求解算法研究.北京林业大学学报,31(S2):9-13.(Wu B G,Ma C.2009b.Design of afforestation and decision-making knowledge base and research of inference engine algorithm in the silviculture expert system.Journal of Beijing Forestry University,31(S2):9-13.[in Chinese])
吴承祯,洪伟.1997.用遗传算法改进约束条件下造林规划设计的研究.林业科学,33(2):38-46.(Wu C Z,Hong W.1997.An improved method of afforestation planning and design under restricted conditions using the genetic algorithm.Scintia Silvae Sinicae,33(2):38-46.[in Chinese])
谢小魁,苏东凯,代力民,等.2011.森林经营决策支持系统的设计与实现及在采伐中的应用.生态学杂志,30(10):2381-2388.(Xie X K,Su D K,Dai L M,et al.2011.Design and actualization of forest management decision support system and its applications in forest harvesting.Chinese Journal of Ecology,30(10):2381-2388.[in Chinese])
俞新妥,林思祖.1986.计算机辅助造林设计系统.林业科学,22(4):337-346.(Yu X T,Lin S Z.1986.A computer-aided design of forest culture.Scintia Silvae Sinicae,22(4):337-346.[in Chinese])
张国梁,蔡小娜,侯晓鹏,等.2016.面向零件的人造板材矩形件锯切排样数学建模及遗传算法求解.林业科学,52(5):150-159.(Zhang G L,Cai X N,Hou X P,et al.2016.Part-oriented cutting layout mathematical modeling and solving by genetic algorithm for rectangular wood based panel parts.Scintia Silvae Sinicae,52(5):150-159.[in Chinese])
Dalemans F,Jacxsens P,Van Orshoven J,et al.2015.Assisting sustainable forest management and forest policy planning with the Sim4Tree decision support system.Forests,6(4):859-878.
Lappi J,Lempinen R.2013.A linear programming algorithm and software for forest-level planning problems including factories.Scandinavian Journal of Forest Research,29(sup1):178-184.
Segura M,Ray D,Maroto C.2014.Decision support systems for forest management:a comparative analysis and assessment.Computers and Electronics in Agriculture,101:55-67.
Uhde B,Hahn W A,Griess V C,et al.2015.Hybrid MCDA methods to integrate multiple ecosystem services in forest management planning:a critical review.Environmental management,56(2):373-388.
Zambelli P,Lora C,Spinelli R,et al.2012.A GIS decision support system for regional forest management to assess biomass availability for renewable energy production.Environmental Modelling&Software,38:203-213.
高飞,刘波,吴保国,等.2013.县级营造林工程管理信息系统的研究与开发.林业资源管理,(3):143-150.(Gao F,Liu B,Wu B G,et al.2013.Study and development of the county-level afforestation project management information system.Forest Resources Management,(3):143-150.[in Chinese])
韩焱云,吴保国,刘建成,等.2014.不确定性推理在森林培育专家系统中的应用.北京林业大学学报,36(1):88-93.(Han Y Y,Wu B G,Liu J C,et al.2014.Application of uncertainty inference in the forest cultivation expert system.Journal of Beijing Forestry University,36(1):88-93.[in Chinese])
亢新刚.2011.森林经理学.北京:中国林业出版社.(Kang X G.2011.Forest management.Beijing:China Forestry Publishing House.[in Chinese])
林晗,洪滔,陈辉,等.2010.应用遗传算法的工业原料林多树种造林设计.林业科学,46(5):92-101.(Lin H,Hong T,Chen H,et al.2010.Multi-species design in planting industrial forests by genetic algorithm.Scientia Silvae Sinicae,46(5):92-101.[in Chinese])
邱荣祖,林雅惠,钟聪儿.2010.基于Arc GIS的木材物流中心选址.林业科学,46(6):113-117.(Qiu R Z,Lin Y H,Zhong C E.2010.Location of timber logistics centers based on Arc GIS.Scintia Silvae Sinicae,46(6):113-117.[in Chinese])
沈立辉,吴保国,杨乃.2014.面状要素主骨架线自动提取算法研究.武汉大学学报:信息科学版,39(7):767-771.(Shen L H,Wu B G,Yang N.2014.Areal feature main skeleton extraction algorithm.Geomatics and Information Science of Wuhan University,39(7):767-771.[in Chinese])
沈立辉.2015.森林资源经营管理平台与经营决策服务子系统的研建.北京:北京林业大学硕士学位论文.(Shen L H.2015.The research and development of forest resources management platform and decision service subsystem.Beijing:MSthesis of Beijing Forestry University.[in Chinese])
温阳,高桂英,杨跃文.2011.内蒙古主要造林树种立地质量评价研究.呼和浩特:内蒙古大学出版社.(Wen Y,Gao G Y,Yang Y W.2011.Study on site quality evaluation of main afforestation tree species in Inner Mongolia.Hohhot:Inner Mongolia University Press.[in Chinese])
吴保国,李成赞,马驰,等.2009a.森林培育专家决策支持系统的研究.北京林业大学学报,31(S2):1-8.(Wu B G,Li C Z,Ma C,et al.2009a.An expert decision support system for silviculture.Journal of Beijing Forestry University,31(S2):1-8.[in Chinese])
吴保国,马驰.2009b.森林培育专家系统中造林决策知识库设计与推理机求解算法研究.北京林业大学学报,31(S2):9-13.(Wu B G,Ma C.2009b.Design of afforestation and decision-making knowledge base and research of inference engine algorithm in the silviculture expert system.Journal of Beijing Forestry University,31(S2):9-13.[in Chinese])
吴承祯,洪伟.1997.用遗传算法改进约束条件下造林规划设计的研究.林业科学,33(2):38-46.(Wu C Z,Hong W.1997.An improved method of afforestation planning and design under restricted conditions using the genetic algorithm.Scintia Silvae Sinicae,33(2):38-46.[in Chinese])
谢小魁,苏东凯,代力民,等.2011.森林经营决策支持系统的设计与实现及在采伐中的应用.生态学杂志,30(10):2381-2388.(Xie X K,Su D K,Dai L M,et al.2011.Design and actualization of forest management decision support system and its applications in forest harvesting.Chinese Journal of Ecology,30(10):2381-2388.[in Chinese])
俞新妥,林思祖.1986.计算机辅助造林设计系统.林业科学,22(4):337-346.(Yu X T,Lin S Z.1986.A computer-aided design of forest culture.Scintia Silvae Sinicae,22(4):337-346.[in Chinese])
张国梁,蔡小娜,侯晓鹏,等.2016.面向零件的人造板材矩形件锯切排样数学建模及遗传算法求解.林业科学,52(5):150-159.(Zhang G L,Cai X N,Hou X P,et al.2016.Part-oriented cutting layout mathematical modeling and solving by genetic algorithm for rectangular wood based panel parts.Scintia Silvae Sinicae,52(5):150-159.[in Chinese])
Dalemans F,Jacxsens P,Van Orshoven J,et al.2015.Assisting sustainable forest management and forest policy planning with the Sim4Tree decision support system.Forests,6(4):859-878.
Lappi J,Lempinen R.2013.A linear programming algorithm and software for forest-level planning problems including factories.Scandinavian Journal of Forest Research,29(sup1):178-184.
Segura M,Ray D,Maroto C.2014.Decision support systems for forest management:a comparative analysis and assessment.Computers and Electronics in Agriculture,101:55-67.
Uhde B,Hahn W A,Griess V C,et al.2015.Hybrid MCDA methods to integrate multiple ecosystem services in forest management planning:a critical review.Environmental management,56(2):373-388.
Zambelli P,Lora C,Spinelli R,et al.2012.A GIS decision support system for regional forest management to assess biomass availability for renewable energy production.Environmental Modelling&Software,38:203-213.