普陀山岛旅游生态安全发展趋势预测
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摘要
科学地预测海岛目的地旅游生态安全发展趋势,对促进海岛旅游经济和生态环境协调发展具有重要的理论意义和实践价值。基于可持续发展的视角,建立了由承载力-支持力-吸引力-延续力和发展力(CSAED模型)子系统构成的普陀山旅游生态安全指标体系,并在灰色系统GM(1,1)模型和RBF神经网络模型比较选优的基础上,对普陀山岛旅游生态安全发展趋势进行了预测。研究结果表明:(1)和灰色系统GM(1,1)模型相比,RBF神经网络模型的Pearson相关系数和误差均方根值更优,可更精确地拟合普陀山岛旅游生态安全发展趋势;(2)2015—2020年,普陀山岛旅游生态安全指数的RBF神经网络模型预测结果由0.7017增加至0.8135,安全等级由比较安全上升至非常安全。研究结果可为维护普陀山岛旅游生态安全提供决策依据。
The study of tourism ecological security is a core problem in the research of sustainable tourism development. It has important theoretical significance and practical value for coordinated development of island tourism economy and the ecological environment. It can be used to scientifically predict island destination development trends of tourism ecological security. Tourism destination can be regarded as an organism with a complex ecosystem. To our knowledge,once the function is disordered,the destination must be considered threatened. The purpose of this paper was to construct a tourism ecological safety index system based on the five subsystems,including "Carrying Capability","Supporting Capability","Attraction Capability","Evolution Capability",and"Developing Capability"( Known as the CSAED model). Based on the sustainable development perspective,and the comprehensive,dynamic principle; this paper uses Mount Putuo Island in Zhejiang Province as an example,and constructs a tourism ecological safety index system based on the subsystems of carrying capacity,attraction capability,evolution capability,and development capability( CSAED model). The paper used the Grey system GM( 1,1) model and the radial basis function( RBF) neural network model to forecast tourism ecological safety in Mount Putuo Island. The results showed that:( 1) both the Pearson correlation coefficient for the RBF neural network and the root mean squared error were better than the Grey System GM( 1,1) model. They also exhibited a better linear fit and a higher precision of prediction. This paper used Grey Relational Analysis to select the main driving factors;and used the results of linear and nonlinear analysis to build equations for trend extrapolation. In addition,based on the results of principal component analysis,the RBF neural network model appeared to provide a new research area for tourism destination ecological security. One of the key issues was that the tourism ecological security situation of Mount Putuo Island became better,because the index of the RBF model from 2005 to 2014 predicted results from 0.3568 to 0.6475. It appeared that the security level increased the sensitivity level,critical level,and the general level. Additionally,the index of the RBF model from 2015 to 2020 predicted results were from 0.7010 to 0.8135,the security level increased from the relatively safe grade to very safe grade. However,it is well known that the ecological system on the island will be influenced by several factors,including natural,social,and economic,among others. In terms of Mount Putuo Island,during the period of the forecast,it may be affected by typhoons,which may affect the vulnerability of the tourism industry. As such,it may lead to deviation from forecasted results. In short,it is suggested that the perspective of natural ecosystems be considered in future research,which would help to construct a better tourism ecological security index system through better mathematical algorithms to enhance the progress of a RBF neural network model. Consequently,the results could provide critical suggestions to scientifically protect tourism ecological security in Mount Putuo Island.
The study of tourism ecological security is a core problem in the research of sustainable tourism development. It has important theoretical significance and practical value for coordinated development of island tourism economy and the ecological environment. It can be used to scientifically predict island destination development trends of tourism ecological security. Tourism destination can be regarded as an organism with a complex ecosystem. To our knowledge,once the function is disordered,the destination must be considered threatened. The purpose of this paper was to construct a tourism ecological safety index system based on the five subsystems,including "Carrying Capability","Supporting Capability","Attraction Capability","Evolution Capability",and"Developing Capability"( Known as the CSAED model). Based on the sustainable development perspective,and the comprehensive,dynamic principle; this paper uses Mount Putuo Island in Zhejiang Province as an example,and constructs a tourism ecological safety index system based on the subsystems of carrying capacity,attraction capability,evolution capability,and development capability( CSAED model). The paper used the Grey system GM( 1,1) model and the radial basis function( RBF) neural network model to forecast tourism ecological safety in Mount Putuo Island. The results showed that:( 1) both the Pearson correlation coefficient for the RBF neural network and the root mean squared error were better than the Grey System GM( 1,1) model. They also exhibited a better linear fit and a higher precision of prediction. This paper used Grey Relational Analysis to select the main driving factors;and used the results of linear and nonlinear analysis to build equations for trend extrapolation. In addition,based on the results of principal component analysis,the RBF neural network model appeared to provide a new research area for tourism destination ecological security. One of the key issues was that the tourism ecological security situation of Mount Putuo Island became better,because the index of the RBF model from 2005 to 2014 predicted results from 0.3568 to 0.6475. It appeared that the security level increased the sensitivity level,critical level,and the general level. Additionally,the index of the RBF model from 2015 to 2020 predicted results were from 0.7010 to 0.8135,the security level increased from the relatively safe grade to very safe grade. However,it is well known that the ecological system on the island will be influenced by several factors,including natural,social,and economic,among others. In terms of Mount Putuo Island,during the period of the forecast,it may be affected by typhoons,which may affect the vulnerability of the tourism industry. As such,it may lead to deviation from forecasted results. In short,it is suggested that the perspective of natural ecosystems be considered in future research,which would help to construct a better tourism ecological security index system through better mathematical algorithms to enhance the progress of a RBF neural network model. Consequently,the results could provide critical suggestions to scientifically protect tourism ecological security in Mount Putuo Island.
引文
[1]胡光宇.蓝色经济发展的混沌条件与经济预测方法.清华大学学报:自然科学版,2011,51(8):1134-1138.
[2]向宝惠,王灵恩.中国海洋海岛旅游发展战略探讨.生态经济,2012,(9):141-145.
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[4]周彬,钟林生,陈田,赵宽.舟山群岛旅游生态健康动态评价.地理研究,2015,34(2):306-318.
[5]秦晓楠,卢小丽,武春友.国内生态安全研究知识图谱——基于Citespace的计量分析.生态学报,2014,34(13):3693-3703.
[6]陈国阶.论生态安全.重庆环境科学,2002,24(3):1-3.
[7]王耕,王利,吴伟.区域生态安全概念及评价体系的再认识.生态学报,2007,27(4):1627-1637.
[8]肖笃宁,陈文波,郭福良.论生态安全的基本概念和研究内容.应用生态学报,2002,13(3):354-358.
[9]马克明,傅伯杰,黎晓亚,关文彬.区域生态安全格局:概念与理论基础.生态学报,2004,24(4):761-768.
[10]李玉照,刘永,颜小品.基于DPSIR模型的流域生态安全评价指标体系研究.北京大学学报:自然科学版,2012,48(6):971-981.
[11]蒙吉军,赵春红,刘明达.基于土地利用变化的区域生态安全评价——以鄂尔多斯市为例.自然资源学报,2011,26(4):578-590.
[12]任志远,刘焱序.基于价值量的区域生态安全评价方法探索——以陕北能源区为例.地理研究,2013,32(10):1771-1781.
[13]赵静静,柴立和,杜慧滨.基于MIEP模型的城市生态安全评价——以宁波市为例.环境科学学报,2015,35(9):2989-2995.
[14]傅伯杰.区域生态环境预警的原理与方法.资源开发与保护,1991,7(3):138-141.
[15]游巍斌,何东进,覃德华,纪志荣,巫丽芸,俞建安,陈炳容,谭勇.世界双遗产地生态安全预警体系构建及应用——以武夷山风景名胜区为例.应用生态学报,2014,25(5):1455-1467.
[16]周彬,钟林生,陈田,周睿.基于变权模型的舟山群岛生态安全预警.应用生态学报,2015,26(6):1854-1862.
[17]徐美,朱翔,刘春腊.基于RBF的湖南省土地生态安全动态预警.地理学报,2012,67(10):1411-1422.
[18]王耕,刘秋波,丁晓静.基于系统动力学的辽宁省生态安全预警研究.环境科学与管理,2013,38(2):144-149.
[19]郑永贤,张智光.森林旅游景区生态安全要素及其景观感知传递性分析.中南林业科技大学学报,2015,35(2):123-129,134-134.
[20]郑永贤,薛菲,张智光.森林旅游景区生态安全IRDS模型实证研究.资源科学,2015,37(12):2350-2361.
[21]吕君,于相贤,刘丽梅.旅游发展生态安全的动力学机制探讨.干旱区资源与环境,2009,23(4):146-149.
[22]Li Y J,Chen T,Hu J,Wang J.Tourism ecological security in Wuhan.Journal of Resources and Ecology,2013,4(2):149-156.
[23]周彬,钟林生,陈田,张爱平.浙江省旅游生态安全的时空格局及障碍因子.地理科学,2015,35(5):599-607.
[24]闫云平,余卓渊,富佳鑫,王文志.西藏景区旅游承载力评估与生态安全预警系统研究.重庆大学学报,2012,35(S1):92-98.
[25]张广海,王佳.海南省旅游开发生态风险评价与预警机制.热带地理,2013,33(1):88-95.
[26]Jing X Y.Ecological security assessment and the sustainable development of tourist destination:a case study of Taining scenic area.Journal of Landscape Research,2010,2(10):61-65,75-75.
[27]肖建红,于庆东,刘康,陈东景,陈娟,肖江南.海岛旅游地生态安全与可持续发展评估——以舟山群岛为例.地理学报,2011,66(6):842-852.
[28]杜忠潮,李志霞.基于生态足迹模型的旅游地生态安全评价研究——以咸阳市为例.西北师范大学学报:自然科学版,2011,47(3):110-115.
[29]武春友,郭玲玲,于惊涛.区域旅游生态安全的动态仿真模拟.系统工程,2013,31(2):94-99.
[30]Liu X L,Yang Z P,Di F,Chen X G.Evaluation on tourism ecological security in nature heritage sites—case of kanas nature reserve of Xinjiang,China.Chinese Geographical Science,2009,19(3):265-273.
[31]郑永贤,张智光.森林旅游景区生态安全影响因素的IRDS模型研究——基于扎根理论.世界林业研究,2015,28(1):23-30.
[32]Rapport D J,Costanza R,Mc Michael A J.Assessing ecosystem health.Trends in Ecology&Evolution,1998,13(10):397-402.
[33]Spiegel J M,Bonet M,Yassi A,Molina E,Concepcion M,Mast P.Developing ecosystem health indicators in Centro Habana:a community-based approach.Ecosystem Health,2001,7(1):15-26.
[34]张鹏,丘萍.岩溶地区旅游生态安全评价及趋势分析——以广西为例.中国岩溶,2014,33(4):483-489.
[35]赵新伟.区域旅游可持续发展的生态安全预警评价研究——以开封市为例.平顶山工学院学报,2007,16(6):13-17.
[36]郁亚娟,郭怀成,刘永,姜玉梅,李艳秋,黄凯.城市病诊断与城市生态系统健康评价.生态学报,2008,28(4):1736-1747.
[37]韦健华,王尔大.基于游客体验效用的旅游承载力评价方法.旅游学刊,2015,30(4):105-114.
[38]Fyall A,Garrod B,Leask A.旅游吸引物管理:新的方向.郭英之主,译.大连:东北财经大学出版社,2005:6-8.
[39]邵琪伟.中国旅游大辞典.上海:上海辞书出版社,2012:417-418.
[40]唐启义.DPS数据处理系统(第三版).北京:科学出版社,2013:634-638.
[41]赵强,徐征和,苏万敏.基于RBF神经网络的城市需水量预测——以济南市为例.水资源与水工程学报,2013,24(6):124-127,132-132.
[42]陈彦光.基于EXCEL的地理数据分析.北京:科学出版社,2010:260-268.
[43]杜栋,庞庆华,吴炎.现代综合评价方法与案例精选(第二版).北京:清华大学出版社,2008:112-115.
[44]陈克龙,苏茂新,李双成,卢京花,陈英玉,张斐,刘志杰.西宁市城市生态系统健康评价.地理研究,2010,29(2):214-222.
[45]李启权,王昌全,岳天祥,李冰,杨娟.基于RBF神经网络的土壤有机质空间变异研究方法.农业工程学报,2010,26(1):87-93.
[2]向宝惠,王灵恩.中国海洋海岛旅游发展战略探讨.生态经济,2012,(9):141-145.
[3]Hall C M.Trends in ocean and coastal tourism:the end of the last frontier?.Ocean&Coastal Management,2001,44(9/10):601-618.
[4]周彬,钟林生,陈田,赵宽.舟山群岛旅游生态健康动态评价.地理研究,2015,34(2):306-318.
[5]秦晓楠,卢小丽,武春友.国内生态安全研究知识图谱——基于Citespace的计量分析.生态学报,2014,34(13):3693-3703.
[6]陈国阶.论生态安全.重庆环境科学,2002,24(3):1-3.
[7]王耕,王利,吴伟.区域生态安全概念及评价体系的再认识.生态学报,2007,27(4):1627-1637.
[8]肖笃宁,陈文波,郭福良.论生态安全的基本概念和研究内容.应用生态学报,2002,13(3):354-358.
[9]马克明,傅伯杰,黎晓亚,关文彬.区域生态安全格局:概念与理论基础.生态学报,2004,24(4):761-768.
[10]李玉照,刘永,颜小品.基于DPSIR模型的流域生态安全评价指标体系研究.北京大学学报:自然科学版,2012,48(6):971-981.
[11]蒙吉军,赵春红,刘明达.基于土地利用变化的区域生态安全评价——以鄂尔多斯市为例.自然资源学报,2011,26(4):578-590.
[12]任志远,刘焱序.基于价值量的区域生态安全评价方法探索——以陕北能源区为例.地理研究,2013,32(10):1771-1781.
[13]赵静静,柴立和,杜慧滨.基于MIEP模型的城市生态安全评价——以宁波市为例.环境科学学报,2015,35(9):2989-2995.
[14]傅伯杰.区域生态环境预警的原理与方法.资源开发与保护,1991,7(3):138-141.
[15]游巍斌,何东进,覃德华,纪志荣,巫丽芸,俞建安,陈炳容,谭勇.世界双遗产地生态安全预警体系构建及应用——以武夷山风景名胜区为例.应用生态学报,2014,25(5):1455-1467.
[16]周彬,钟林生,陈田,周睿.基于变权模型的舟山群岛生态安全预警.应用生态学报,2015,26(6):1854-1862.
[17]徐美,朱翔,刘春腊.基于RBF的湖南省土地生态安全动态预警.地理学报,2012,67(10):1411-1422.
[18]王耕,刘秋波,丁晓静.基于系统动力学的辽宁省生态安全预警研究.环境科学与管理,2013,38(2):144-149.
[19]郑永贤,张智光.森林旅游景区生态安全要素及其景观感知传递性分析.中南林业科技大学学报,2015,35(2):123-129,134-134.
[20]郑永贤,薛菲,张智光.森林旅游景区生态安全IRDS模型实证研究.资源科学,2015,37(12):2350-2361.
[21]吕君,于相贤,刘丽梅.旅游发展生态安全的动力学机制探讨.干旱区资源与环境,2009,23(4):146-149.
[22]Li Y J,Chen T,Hu J,Wang J.Tourism ecological security in Wuhan.Journal of Resources and Ecology,2013,4(2):149-156.
[23]周彬,钟林生,陈田,张爱平.浙江省旅游生态安全的时空格局及障碍因子.地理科学,2015,35(5):599-607.
[24]闫云平,余卓渊,富佳鑫,王文志.西藏景区旅游承载力评估与生态安全预警系统研究.重庆大学学报,2012,35(S1):92-98.
[25]张广海,王佳.海南省旅游开发生态风险评价与预警机制.热带地理,2013,33(1):88-95.
[26]Jing X Y.Ecological security assessment and the sustainable development of tourist destination:a case study of Taining scenic area.Journal of Landscape Research,2010,2(10):61-65,75-75.
[27]肖建红,于庆东,刘康,陈东景,陈娟,肖江南.海岛旅游地生态安全与可持续发展评估——以舟山群岛为例.地理学报,2011,66(6):842-852.
[28]杜忠潮,李志霞.基于生态足迹模型的旅游地生态安全评价研究——以咸阳市为例.西北师范大学学报:自然科学版,2011,47(3):110-115.
[29]武春友,郭玲玲,于惊涛.区域旅游生态安全的动态仿真模拟.系统工程,2013,31(2):94-99.
[30]Liu X L,Yang Z P,Di F,Chen X G.Evaluation on tourism ecological security in nature heritage sites—case of kanas nature reserve of Xinjiang,China.Chinese Geographical Science,2009,19(3):265-273.
[31]郑永贤,张智光.森林旅游景区生态安全影响因素的IRDS模型研究——基于扎根理论.世界林业研究,2015,28(1):23-30.
[32]Rapport D J,Costanza R,Mc Michael A J.Assessing ecosystem health.Trends in Ecology&Evolution,1998,13(10):397-402.
[33]Spiegel J M,Bonet M,Yassi A,Molina E,Concepcion M,Mast P.Developing ecosystem health indicators in Centro Habana:a community-based approach.Ecosystem Health,2001,7(1):15-26.
[34]张鹏,丘萍.岩溶地区旅游生态安全评价及趋势分析——以广西为例.中国岩溶,2014,33(4):483-489.
[35]赵新伟.区域旅游可持续发展的生态安全预警评价研究——以开封市为例.平顶山工学院学报,2007,16(6):13-17.
[36]郁亚娟,郭怀成,刘永,姜玉梅,李艳秋,黄凯.城市病诊断与城市生态系统健康评价.生态学报,2008,28(4):1736-1747.
[37]韦健华,王尔大.基于游客体验效用的旅游承载力评价方法.旅游学刊,2015,30(4):105-114.
[38]Fyall A,Garrod B,Leask A.旅游吸引物管理:新的方向.郭英之主,译.大连:东北财经大学出版社,2005:6-8.
[39]邵琪伟.中国旅游大辞典.上海:上海辞书出版社,2012:417-418.
[40]唐启义.DPS数据处理系统(第三版).北京:科学出版社,2013:634-638.
[41]赵强,徐征和,苏万敏.基于RBF神经网络的城市需水量预测——以济南市为例.水资源与水工程学报,2013,24(6):124-127,132-132.
[42]陈彦光.基于EXCEL的地理数据分析.北京:科学出版社,2010:260-268.
[43]杜栋,庞庆华,吴炎.现代综合评价方法与案例精选(第二版).北京:清华大学出版社,2008:112-115.
[44]陈克龙,苏茂新,李双成,卢京花,陈英玉,张斐,刘志杰.西宁市城市生态系统健康评价.地理研究,2010,29(2):214-222.
[45]李启权,王昌全,岳天祥,李冰,杨娟.基于RBF神经网络的土壤有机质空间变异研究方法.农业工程学报,2010,26(1):87-93.
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