四川省地级市生态环境质量评价模型

在建立四川省生态质量评价指标体系的基础上,将径向基函数（RBF）神经网络模型用于四川省18个地级市生态质量评价和区划,实现了评价结果的可视化与直观化。评价结果与四川省生态质量实际状况相对比,表明了RBF网络的正确性和实用性,为实现四川省生态质量的保护提供了一条新途径。     

几种神经网络模型在空气质量评价中的应用

人工神经网络是一种利用计算机模拟人脑神经组织的运算模型。作为一种新的研究方法。神经网络在一定程度上可以弥补传统评价方法需要构建隶属函数、无法精确描述级别区间内的变化特征以及设计过程具有一定人为偏好的不足。本文选用BP网络、径向基函数网络、LVQ网络和Elman网络这4种典型的神经网络模型进行实例研究。把国内8个城市的污染物排放数据代入训练好的网络模型，进行空气质量评价，得出的结论对提高神经网络评价结果的准确性和可靠性有一定参考作用。     

RBF网络在土地资源评价中的应用

以农地分级标准作为基础样本，构建了径向基网络模型，并利用该网络进行了实际评价。结果表明：RBF网络计算精度高，简便有效，可操作性强，具有极快的收敛速度和分类能力，与其它方法比较，不需要烦琐的计算过程，节约了大量计算时间，并且RBF网络具有良好的泛化能力，适用性广，在土地资源评价中具有良好的应用前景。     

区域生态环境预警的原理与方法

本文提出了以区域持续发展的能力作为区域生态环境预警的综合指标,进一步用承载力、稳定性、缓冲力、生产力和调控力作为衡量区域持续发展的能力。选取生态、环境和社会经济指标对区域生态环境质量进行评价,以及对经济发展的协调性和适应性进行分析,作出判断和预警,并讨论了区域生态环境预警的综合方法。     

煤与瓦斯突出预测的GA-SVM模型及应用

为了对煤与瓦斯突出进行有效预测,将遗传算法和支持向量机相结合,提出煤与瓦斯突出预测的GA-SVM模型。以我国典型的煤与瓦斯突出煤矿15个实例为样本,以交叉验证准确率作为遗传算法的适应度函数,搜索得到径向基核SVM最优惩罚因子C=28.8786、宽度函数σ=0.16508,利用最优参数建立煤与瓦斯突出预测GA-SVM模型进行预测,结果与实际完全一致。应用该模型对云南恩洪煤矿8个突出实例进行预测,并与单项指标法、综合指标法和BP神经网络进行比较。研究结果表明,煤与瓦斯突出预测GA-SVM模型具有较高的可靠性和精确性,能对煤与瓦斯突出进行有效预测。     

区域生态文明建设系统分析与建模

根据区域生态文明建设的特征、内容和目标,通过系统分析,将其动态过程、因果反馈、评价系统及优化调控等归结为多目标非线性动态规划问题,将其运行空间表示成一个三维结构,用数学与电脑相结合的方法进行系统建模与动态仿真。结果表明,该研究对形成区域生态文明建设的科学决策和调控机制、透视发展前景、加强能力建设等具有现实意义,也充实和完善了有关区域生态文明评价的理论和方法。     

生态文明评价的文献综述及其未来研究趋势

运用文献分析和比较分析法系统的梳理了关于生态文明评价的研究文献,阐释了已有生态文明评价研究存在的不足:评价范围以宏观、中观区域为主,微观区域相对较少;指标未能凸显生态文明评价的独特性和专有性,指标权重未能突出产业与生态的互利共生性;评价方法未能兼顾指标法和指数法的优势;根据"单一"综合得分高低或综合指数大小对区域生态文明进行排名;有些未"入门"区域,如北京、上海等参与了生态文明排名且排名靠前,而云南、海南等排名靠后,与省域生态文明建设实际情况不相吻合。据此归纳了未来研究趋势:趋于探索科学化方法来确定生态文明指标及其权重;趋于探索兼顾指标和指数优势的方法对生态文明进行评价,用合乎实际的多元化标准来判定生态文明水平。     

成都市生态足迹分析评价研究

生态足迹分析法是一种全新的生态承载力计算方法.从生态足迹分析原理出发,分析统计了成都市生物资源消费、碳吸收地、建设用地的生态足迹,得到人均生态赤字为1.183gha,属于较严重生态赤字区的结论.结果表明,该区域对自然的影响超出了其生态承载能力的范围,需要进行调整.     

三百山禁止开发区生态旅游环境质量综合评价

对主体功能区中禁止开发区域进行生态旅游环境评价是发展生态旅游的前提.选取国家风景名胜区三百山为研究对象,采用AHP法构建生态旅游环境质量评价指标体系,通过压力-状态-响应模型,应用模糊综合评判法定量分析了该区域生态旅游活动对自然、经济社会环境质量的影响,以及生态压力与响应状况.评价结果为禁止开发区域探寻生态旅游发展模式提供了决策依据和参考.     

生态补偿的评价

研究了如何识别和评价生态补偿好坏及程度,如何改进生态补偿;探讨了补偿失效的成因、机制、表现.在此基础上,进一步研究了生态补偿有效的因素、功能机制和特征,生态补偿评价的依据、标准和评价过程的原理、方法、技术、程序,以及补偿评价结果的分类.     

修正的BP神经网络森林火灾成灾面积预测研究

以1997-2012年《中国林业统计年鉴》的全国森林火灾成灾面积为依据,应用BP神经网络模型对成灾面积进行了预测,对预测方法进行了检验.在此基础上,利用残差提出了修正的BP神经网络模型,并对预测方法进行了改进.研究结果表明,修正的BP神经网络预测精度高于BP神经网络,预测相对误差平均为7.2％,可应用于森林火灾成灾面积的预测.     

基于ANN的环境质量评价

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理，具有自学习和自适应等一系列优点，因而用它来评价环境质量是可行的。本文针对环境质量评价问题，建立了基于神经网络的评价系统，给出了应用实例。     

Performance enhancement of a stand-alone induction generator-based wind energy system using neural network controller

ABSTRACT

The limitation of self-excited induction generator (SEIG) when used in the stand-alone wind energy system (WES) is poor voltage regulation at variable speed. The indirect vector control (IVC) technique is employed for both the generator-side converter (GSC) and load-side converter (LSC) to regulate the variation of SEIG speed, DC link voltage, and electromagnetic torque independently. Further performance of the proposed IVC technique has been analyzed independently with neural network controller (NNC) and fuzzy logic controller (FLC) as its components. The FLC is replaced by an NNC to improve the performance of the proposed system. IVC of SEIG-based WES has been simulated in MATLAB/SIMULINK software, and the prototype model of the proposed WES is developed to experimentally validate the performance using dSPACE DS-1104 R&D controller board.     

基于因子分析的BP神经网络对成都市需水量的预测

在对四川省成都市的水资源进行综合规划与评价的过程中,通过主成分因子分析可知人口、GDP、给排水管道长度等因素对需水量预测有较大影响,如何建立这些因素与需水量之间的数学关系是预测工作的重点。本文将介绍通过MATLAB数学分析软件建立BP神经网络预测模型,并对模型的预测结果进行评价。     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

Application of Artificial Neural Networks to Predict Total Dissolved Solids at the Karaj Dam

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural networks using data from two water quality monitoring stations at the Karaj Dam in Iran. Input data were calcium ions (Ca²⁺), magnesium ions (Mg²⁺), sodium ions (Na⁺), chloride ions (Cl^?), sulfate (), and pH, and the output data were total dissolved solids (TDS). An MLP with one hidden layer containing eight neurons was selected for the upstream water quality station using normalized input data. We developed a second MLP neural network for the downstream station with one hidden layer containing 10 neurons in the hidden layer using normalized input data. Considering applying normalized input data and one hidden layer, the coefficient of determination (R ²) and index of agreement (IA) between the observed and the predicted data for the upstream and downstream monitoring stations using the MLP neural networks were 0.985, 0.84, 0.99, and 0.92, respectively. The RBF neural network with 100 neurons in its hidden layer reached the minimum errors between the observed and the predicted results in upstream and downstream stations. The R ² between observed and predicted data for upstream and downstream monitoring stations for the RBF was 0.999 and 0.998, respectively. Data normalization improved the performance of the MLP neural networks. Sensitivity analysis indicated that magnesium is the most effective water quality parameter for predicting TDS, and sulfate is the second most effective water quality parameter affecting TDS prediction at the Karaj Dam.     

一种改进的模糊聚类方法在大气环境质量评价中的应用

一般的模糊聚类分析方法只能解决大气污染状况的顺序问题,或只能得出此地与彼地的大气质量状况的相似程度,而不能同时确定大气质量的具体等级。本文提出的一种改进的模糊聚类分析方法解决了这一问题、该方法的主要改进在于:①以各污染因子的污染程度的分级标准值为聚类中心,待分样本与聚类中心之间的相似系数或待分样本隶属于某一类的隶属函数作为聚类函数;②相似系数设计为几何平均最小型,隶属函数设计为正态分布型。实例计算和比较表明,该方法是大气环境质量评价中的一种较好的方法。     

基于人工神经网络模型预测2017~2018年成都市危险废物增量研究

危险废物对环境或者人体健康会造成有害影响,有效地预测其产量是优化管理和合理处置的重要依据。以2008~2016年成都市危险废物产生量为基础,通过数据带入和整合及综合各参数因子的影响,利用人工神经网络模型预测方法客观反映并预测成都市危废产量的变化趋势。结果表明该模型预测2017~2018年成都市危险废物年产量分别达到24.46万t和26.88万t,模拟精度偏差低。因此,人工神经网络模型可以作为一种预测危险废物产生量的工具,其预测结果可以为职能部门提供决策参考。     

A first alternative for estimating the physical carrying capacities of natural areas for recreation

Utilizing an adaptation of the Universal Soil Loss Equation, this article discusses a method for approximating the physical carrying capacity of natural areas for outdoor recreation. Classification of forested woodland and field environments is based upon the conversion of ground cover coefficients to the percentage of ground cover required to maintain soil productivity over time. Four canopy types, three canopy densities, and two general types of ground cover are recognized in the equation as well as soil characteristics, topographical variations, and rainfall velocities and intensities. The method requires that the areal distribution of soils occurring within natural areas be mapped. Approximations will vary according to the intensity of the planning desired, and may range from a general classification of large land areas to highly site-specific evaluations. Data generated from over 40 years of cooperative research form the basis for classifying natural areas according to their relative physical capacities to accommodate outdoor recreation.     

基于Adaboost集成BP网络原煤生产成本预测研究

为了提高传统BP神经网络预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络与Ada-boost算法相结合,提出了一种Adaboost集成BP神经网络模型.结合磁县观台煤矿原煤生产成本相关数据,建立了原煤生产成本预测的Adaboost集成BP神经网络模型,将该模型用于实际的原煤成本预测.结果表明：该模型预测精度高于传统的BP神经网络,收敛速度快,具有较强的鲁棒性,预测精度能满足实际预测需要,为原煤生产成本预测提供了一种新的途径,也为原煤生产成本控制提供了重要依据.