基于Logistic回归的城市资源环境承载力预测研究——以武汉市为例
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摘要
资源环境是人类赖以生存和发展的基础,准确可靠的资源环境承载力预测不仅是城市综合承载力风险评估、预警的需要,也是政府对新型城镇化政策制定的重要依据。选取武汉市2002-2015年的相关数据作为样本,先利用熵权TOPSIS模型得出武汉市的资源环境承载力综合指数,再运用逻辑回归模型对武汉市资源环境承载力进行预测。将预测值与实际值进行比较发现逻辑回归模型的拟合度更好,精确度更高,平均相对误差为1.57%。最后将逻辑回归模型与现有方法相比较,验证了逻辑回归模型在承载力预测方面的有效性。
Resources and environment are the basis for human survival and development.Accurate and reliable prediction of resource and environmental carrying capacity is not only the need for risk assessment and early warning of urban comprehensive carrying capacity,but also an important basis for the government to formulate new urbanization policies.Taking the relevant data of Wuhan City from 2002 to 2015 as a sample,the comprehensive index of resources and environmental carrying capacity of Wuhan City was first obtained by using the TOPSIS model of entropy,and then the logistic regression model was used to predict the resource carrying capacity of Wuhan City.Comparing the predicted value with the actual value found that the logistic regression model has better fitting accuracy and higher accuracy,with an average relative error of 1.57%.Finally,the logistic regression model was compared with the existing method to verify the validity of the logistic regression model in predicting the bearing capacity.
Resources and environment are the basis for human survival and development.Accurate and reliable prediction of resource and environmental carrying capacity is not only the need for risk assessment and early warning of urban comprehensive carrying capacity,but also an important basis for the government to formulate new urbanization policies.Taking the relevant data of Wuhan City from 2002 to 2015 as a sample,the comprehensive index of resources and environmental carrying capacity of Wuhan City was first obtained by using the TOPSIS model of entropy,and then the logistic regression model was used to predict the resource carrying capacity of Wuhan City.Comparing the predicted value with the actual value found that the logistic regression model has better fitting accuracy and higher accuracy,with an average relative error of 1.57%.Finally,the logistic regression model was compared with the existing method to verify the validity of the logistic regression model in predicting the bearing capacity.
引文
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[3]孙林,杨世元,吴德会.基于LS-SVM城市水资源承载能力预测方法[J].水科学与工程技术,2008(S2):34-37.
[4]张灵,陈晓宏,刘丙军,等.基于AGA的SVM需水预测模型研究[J].水文,2008(1):38-42,46.
[5]张晓娟,周启刚.基于熵权TOPSIS和灰色模型的土地承载力评价与预测——以三峡库区为例[J].资源开发与市场,2017,33(6):666-671.
[6]李目,谭文,何怡刚,等.控制Logistic系统的自适应Chebyshev多项式神经网络算法[J].电子测量与仪器学报,2010,24(8):730-736.
[7]旷开金.福建省环境承载力综合评估与预测研究[D].福州:福建农林大学,2015.
[8]宰松梅,温季,仵峰,等.河南省新乡市水资源承载力评价研究[J].水利学报,2011,42(7):783-788.
[9]雷勋平,邱广华.基于熵权TOPSIS模型的区域资源环境承载力评价实证研究[J].环境科学学报,2016,36(1):314-323.
[10]杨云龙,周小成,吴波.基于时空Logistic回归模型的漳州城市扩展预测分析[J].地球信息科学学报,2011,13(3):374-382.
[11]李明阳,陈万忠,张涛.基于DD-DWT和Log-Logistic参数回归的癫痫脑电自动识别方法[J].仪器仪表学报,2017,38(6):1368-1377.
[12]吕斌,孙莉,谭文垦.中原城市群城市承载力评价研究[J].中国人口.资源与环境,2008(5):53-58.
[13]程慧.支持向量机优化算法及其在城市环境承载力中的应用[D].兰州:甘肃农业大学,2013.