基于RBF神经网络模型的资源型城市生态安全预警——以榆林市为例
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摘要
运用SDI(状态—胁迫—免疫)模型构建资源型城市生态安全预警指标体系,基于RBF神经网络模型及警情评判标准对榆林市2010—2016年的生态安全状况进行分析,并对榆林市2017—2021年的生态安全演化趋势进行动态预测。研究结果表明:(1)RBF神经网络模型能够有效地用于资源型城市生态安全预警分析,拟合精度较高;(2)2010—2016年,榆林市整体生态安全警情指数不断上升,但是仍处于临界安全状态;(3)根据未来趋势预测结果,到2020年,榆林市生态安全"状态"子系统警情指数将有大幅提升,"胁迫"子系统生态安全警情指数呈略微下降趋势,"免疫"子系统呈波动趋势。由此表明,快速提升榆林市生态安全状况,首先要改善胁迫子系统的状况,其次是加强免疫子系统的免疫能力,据此提出了针对榆林市提升生态安全状况的四条响应措施。
The SDI(state-stress-immunization) model was used to construct a resource-based urban ecological security early warning indicator system. Based on the RBF neural network model and the police evaluation criteria, the ecological security status of Yulin City from 2010 to 2016 was analyzed, and the ecological security evolution trend of Yulin City from2017 to 2021 is dynamically predicted. The results show that:(1) RBF neural network model can be effectively used for resource-based urban ecological security early warning analysis, and the fitting accuracy is high;(2) In 2010-2016, the overall ecological security early warning index of Yulin City continues to rise, but still In the critical security state;(3) According to the forecast of future trends, by 2020, the warning index of the "state" subsystem of the ecological security of Yulin City has been greatly improved, and the ecological security early warning index of the "stress" subsystem has a slight downward trend,"immune" subsystems are fluctuating. This indicates that the rapid improvement of the ecological security status of Yulin City must first improve the status of the stress subsystem, followed by strengthening the immunity of the immune system. Based on this, four response measures for improving the ecological security status of Yulin City are proposed.
The SDI(state-stress-immunization) model was used to construct a resource-based urban ecological security early warning indicator system. Based on the RBF neural network model and the police evaluation criteria, the ecological security status of Yulin City from 2010 to 2016 was analyzed, and the ecological security evolution trend of Yulin City from2017 to 2021 is dynamically predicted. The results show that:(1) RBF neural network model can be effectively used for resource-based urban ecological security early warning analysis, and the fitting accuracy is high;(2) In 2010-2016, the overall ecological security early warning index of Yulin City continues to rise, but still In the critical security state;(3) According to the forecast of future trends, by 2020, the warning index of the "state" subsystem of the ecological security of Yulin City has been greatly improved, and the ecological security early warning index of the "stress" subsystem has a slight downward trend,"immune" subsystems are fluctuating. This indicates that the rapid improvement of the ecological security status of Yulin City must first improve the status of the stress subsystem, followed by strengthening the immunity of the immune system. Based on this, four response measures for improving the ecological security status of Yulin City are proposed.
引文
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[2]肖建红,王敏,于庆东,等.基于生态足迹的大型水电工程建设生态补偿标准评价模型--以三峡工程为例[J].生态学报,2015(8):2726-2740.
[3]张智光.林业生态安全的共生耦合测度模型与判据[J].中国人口·资源与环境,2014(8):90-99.
[4]龚建周,夏北成,陈健飞.快速城市化区域生态安全的空间模糊综合评价--以广州市为例[J].生态学报,2008(10):4992-5001.
[5]顾立忠,宫鹏杰,李虎成,等.北江流域水污染预警体系研究初探[J].中国水运月刊,2016(12):155-157.
[6]Sleeser M.Enhancement of carrying capacity options ECCO[M].London:The Resource Use Institute,1990.
[7]Tilman D,Fargione J,Wolff B,et al.Forecasting agriculturally driven global environmental change[J].Science,2001292(5515):281-284.
[8]周彬,钟林生,陈田,等.基于变权模型的舟山群岛生态安全预警[J].应用生态学报,2015(6):1854-1862.
[9]马世五,谢德体,张孝成,等.三峡库区生态敏感区土地生态安全预警测度与时空演变--以重庆市万州区为例[J].生态学报,2017(24):8227-8240.
[10]徐美,朱翔,刘春腊.基于RBF的湖南省土地生态安全动态预警[J].地理学报,2012(10):1411-1422.
[11]张强,薛惠锋,张明军,等.基于可拓分析的区域生态安全预警模型及应用--以陕西省为例[J].生态学报,2010(16):4277-4286.
[12]王军广,赵志忠,赵广孺,等.海南岛土地生态安全评价[J]安徽农业科学,2009(8):4215-4218.
[13]Cheng Q,Zhou L F,Zhang Y L.Ecological environment evaluation of Liaohe delta wetland based on PSR model[J]Advanced Materials Research,2012,518-523:78-80.
[14]肖笃宁,解伏菊,魏建兵.区域生态建设与景观生态学的使命[J].应用生态学报,2004(10):1731-1736.
[15]张利,陈影,王树涛,等.滨海快速城市化地区土地生态安全评价与预警--以曹妃甸新区为例[J].应用生态学报,2015(8):2445-2454.
[16]曲艺,陆明.生物多样性保护视角下的城市生态安全格局构建研究[J].城市发展研究,2017(4):C10-C13.
[17]张玉泽,任建兰,刘凯,等.山东省生态安全预警测度及时空格局[J].经济地理,2015(11):166-171,189.
[18]赵宏波,马延吉.基于变权-物元分析模型的老工业基地区域生态安全动态预警研究--以吉林省为例[J].生态学报,2014(16):4720-4733.
[19]余敦,高群,欧阳龙华.鄱阳湖生态经济区土地生态安全警情研究[J].长江流域资源与环境,2012(6):678-683.
[20]张洪,王安琦,宋贝扬.基于OWA的大理市土地生态安全评价研究[J].地理科学,2017(11):1778-1784.
[21]薛雅伟,张在旭,李宏勋,等.资源产业空间集聚与区域经济增长:“资源诅咒”效应实证[J].中国人口·资源与环境,2016(8):25-33.
[22]蒋妮姗.城市生态与环境安全评价研究--以苏州市为例[D]苏州:苏州科技学院,2008.
[23]吕光辉.中国西部干旱区生态安全评价、预警与调控研究[D]乌鲁木齐:新疆大学,2005.
[24]吉力力·阿不都外力,木巴热克·阿尤普.基于生态足迹的中亚区域生态安全评价[J].地理研究,2008(6):1308-1320.
[25]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[J].生态学报,2014(13):3693-3703.
[26]张红丽,滕慧奇.林业生态安全预警测度与技术干预分析[J]科技管理研究,2017(19):246-252.
[27]张晓瑞,方创琳,王振波,等.基于RBF神经网络的城市建成区面积预测研究--兼与BP神经网络和线性回归对比分析[J]长江流域资源与环境,2013(6):691-697.
[28]陈美婷,匡耀求,黄宁生.基于RBF模型的广东省土地生态安全时空演变预警研究[J].水土保持研究,2015(3):217-224.
[29]孙晓鸣,柏益尧,左玉辉.生态城市评价中的RBF神经网络模型--以厦门市为例[J].环境保护科学,2005(5):43-58.
[30]陈英,孔喆,路正,等.基于RBF神经网络模型的土地生态安全预警--以甘肃省张掖市为例[J].干旱地区农业研究,2017(1):264-270.