發(fā)布時(shí)間：2018-02-04 01:28

  本文關(guān)鍵詞： 城市承載力 相空間重構(gòu) 支持向量機(jī) 粒子群算法　出處：《浙江工商大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著城市化進(jìn)程的加快和社會經(jīng)濟(jì)快速發(fā)展,很多城市盲目發(fā)展和建設(shè),城市資源利用效率低下,導(dǎo)致土地浪費(fèi)現(xiàn)象突出、淡水和能源資源緊張、環(huán)境污染嚴(yán)重、生態(tài)系統(tǒng)嚴(yán)重退化、交通擁堵日益嚴(yán)重等城市危機(jī)險(xiǎn)象頻生。而城市承載力的大小嚴(yán)重關(guān)系到城市能否持續(xù)健康的發(fā)展,人們能否享受更高的生活質(zhì)量。因此,人們迫切希望尋求一種切實(shí)有效的模型對城市承載力進(jìn)行預(yù)測,進(jìn)而對人們的生活和政府的規(guī)劃進(jìn)行有效的指導(dǎo)。由此本文將城市水資源、土地資源、交通和大氣環(huán)境作為研究的重點(diǎn)對象,結(jié)合統(tǒng)計(jì)年鑒和水資源公報(bào)等發(fā)掘已有的城市數(shù)據(jù),分別構(gòu)建城市水資源承載力評價(jià)指標(biāo)、土地資源承載力評價(jià)指標(biāo)、交通承載力評價(jià)指標(biāo)、大氣環(huán)境承載力評價(jià)指標(biāo)和綜合承載力評價(jià)指標(biāo),以期為解決城市在快速發(fā)展的同時(shí)衍生出的諸多問題提供方向。此外,為了較全面展現(xiàn)我國中心城市目前的城市承載力發(fā)展?fàn)顩r,本文基于選取的評價(jià)指標(biāo),選擇了京津冀、長三角、珠三角等重要區(qū)域及直轄市、省會城市和計(jì)劃單列市共64個(gè)城市來研究其承載力現(xiàn)狀。研究發(fā)現(xiàn):2014年度,我國城市綜合承載力普遍較差。具體來說,逾50%的城市水資源承載力處于預(yù)警甚至危機(jī)的狀態(tài);逾56%的城市的土地資源承載力處于危機(jī)狀態(tài);80%左右的城市交通承載力極其脆弱;空氣質(zhì)量達(dá)優(yōu)的城市僅有一個(gè),占1.56%。這一切都表明:我國城市的城市承載力已經(jīng)受到了嚴(yán)重的挑戰(zhàn),人們的正常生活和社會的健康發(fā)展已經(jīng)受到了嚴(yán)重的影響。本文針對這一現(xiàn)象,以基于有限數(shù)據(jù)的機(jī)器學(xué)習(xí)方法——支持向量機(jī)(Support Vector Machine,SVM),從四個(gè)方面的城市承載力歷史數(shù)值角度出發(fā)對未來的相應(yīng)的城市承載力數(shù)值進(jìn)行預(yù)測。起初,分別對這四個(gè)承載力單列的時(shí)間序列數(shù)據(jù)進(jìn)行相空間重構(gòu)生成時(shí)序矩陣,擴(kuò)大信息量,確定水資源、土地資源、交通和大氣環(huán)境它們各自的承載力最優(yōu)嵌入維度分別為4、5、2、6.然后運(yùn)用支持向量回歸模型對時(shí)序矩陣中的數(shù)據(jù)進(jìn)行建模。鑒于模型結(jié)果會因?yàn)橹С窒蛄炕貧w機(jī)參數(shù)的選擇不同導(dǎo)致結(jié)果差異明顯,本文依據(jù)核函數(shù)參數(shù)敏感度強(qiáng)于核函數(shù)敏感度的理論,采取以下兩種方式選擇參數(shù)來提高支持向量機(jī)回歸預(yù)測模型效果:其一,直接使用支持向量機(jī)中默認(rèn)參數(shù);其二,采用粒子群優(yōu)化算法(PSO)擇優(yōu)選取懲罰因子與核參數(shù)。模型效果對比結(jié)果顯示PSO-SVM比一般SVM的預(yù)測結(jié)果更加精確,其實(shí)用性更好。之后使用PSO-SVM模型對杭州市未來五年的水資源、土地資源、交通承載力以及大氣環(huán)境承載力進(jìn)行預(yù)測。結(jié)果顯示未來五年杭州市的綜合承載能力呈現(xiàn)下降趨勢,水資源和大氣環(huán)境承載力處于較好的狀態(tài),一般將不會對杭州市的發(fā)展產(chǎn)生較壞的影響。而土地資源承載力和交通承載力則相對比較低下,最有可能對杭州市未來發(fā)展可能造成阻礙。
[Abstract]:With the acceleration of urbanization and the rapid development of social economy, many cities are blindly developing and building, urban resource utilization efficiency is low, leading to the phenomenon of land waste, freshwater and energy resources are tight. Serious environmental pollution, serious degradation of ecosystem, traffic congestion and other urban crisis risk frequently. And the size of urban bearing capacity is related to the sustainable and healthy development of the city. Whether people can enjoy a higher quality of life, therefore, people are eager to find a practical and effective model to predict the urban carrying capacity. Therefore, this paper focuses on urban water resources, land resources, traffic and atmospheric environment. Combined with the statistical yearbook and water resources bulletin to excavate the existing urban data, respectively to build the urban water resources carrying capacity evaluation index, land resources carrying capacity evaluation index, traffic carrying capacity evaluation index. The evaluation index of atmospheric environmental carrying capacity and the comprehensive carrying capacity evaluation index are expected to provide the direction for solving many problems arising from the rapid development of the city at the same time. In order to fully show the current development of urban bearing capacity of central cities in China, this paper selected the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and other important regions and municipalities directly under the Central Government based on the selected evaluation indicators. There are 64 cities in provincial capitals and planned cities to study the status of carrying capacity. The study found that the comprehensive carrying capacity of cities in China is generally poor in the year of 2014. To be specific, the comprehensive carrying capacity of cities in China is generally poor. More than 50% of the urban water resources carrying capacity is in the state of early warning or even crisis; The carrying capacity of land resources in more than 56% cities is in a state of crisis. About 80% of the urban traffic carrying capacity is extremely fragile; There is only one city with excellent air quality, accounting for 1.56%. All this shows that the urban carrying capacity of Chinese cities has been seriously challenged. The normal life of people and the healthy development of society have been seriously affected. Support vector machine support Vector machine (SVM) is used as a machine learning method based on finite data. From four aspects of the historical value of urban bearing capacity from the point of view of the future of the corresponding urban carrying capacity of the prediction. At first. The time series data of these four single columns of bearing capacity are reconstructed to generate time series matrix to enlarge the amount of information and to determine the water resources and land resources. The optimal embed dimensions of transport and atmospheric environment are respectively 4? 5? 2??? 6. Then the support vector regression model is used to model the data in the time series matrix. Based on the theory that kernel function parameter sensitivity is stronger than kernel function sensitivity, this paper adopts the following two ways to select parameters to improve support vector machine regression prediction model effect: first. Direct use of default parameters in support vector machines; Secondly, the particle swarm optimization (PSO) algorithm is used to select the penalty factor and kernel parameter. The model results show that PSO-SVM is more accurate than the general SVM. Its practicability is better. Then the PSO-SVM model is used to analyze the water resources and land resources of Hangzhou in the next five years. The results show that the comprehensive bearing capacity of Hangzhou city in the next five years shows a downward trend, water resources and atmospheric environmental carrying capacity is in a better state. Generally, it will not have a bad effect on the development of Hangzhou, but the carrying capacity of land resources and traffic is relatively low, which is most likely to hinder the future development of Hangzhou.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：C912.81;C812

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