智能物联网在环境在线监控中的应用研究

目前的环境在线监控网络,虽然已经具备了物联网的一些基本特征,并且具备一定环境反应、故障诊断以及应急处理能力,但智能化程度普遍不高,仅停留在了简单的被动式反应,没有实现真正意义上与多智能体技术的结合,所以对于环境监测数据利用还不够充分,深度和广度还有待扩展。本文提出了一种多智能技术与环境在线监控物联网结合的设计方法,以及由此产生的对环境保护工作的提升作用。     

广州城市电网工频电磁环境影响分析及建议

高压输变电工程进入城市中心地带是发展的趋势,而工频电磁环境是居民关注的问题,根据目前广州城市电网建设的现状,对广州城区110 kV、220 kV各类型变电站、多种形式的输电线路产生的工频电磁环境影响进行分析,对比国家现行标准限值,评价广州城市电网工频电磁环境影响的总体情况,认识电网的未来发展趋势,并结合输变电电磁环境影响特点提出相应广州电网建设的意见与建议。     

Method to map human and infrastructure vulnerability using CNN land cover: Case study of floating tank explosion at petrochemical plants of LaemChabang,Thailand

Industrial storage tanks, used to store flammable materials in the petrochemical industry, can induce potential fire and explosion under specific conditions. Therefore, it is necessary to map the population and environment vulnerability, and, to develop procedures for emergency responses in order to reduce potential casualties. In order to achieve this, Convolutional Neural Networks (CNN) are used in this study using 6 classes: floating tank, forest, house, road, wasteland and water. Datasets are built for a total of approximately 1.4 million tiles with a resolution of 0.33m/pixel and their size are optimized in function of the class. The 6 associated CNN models are built and optimized to classify each class. The validation of the models shows that, with the exception of road and wasteland where the precision is only 73% and 89% respectively, the other 4 classes have a value higher than 95%. Post-processing is performed on each prediction before aggregating these results to obtain the land cover. For the floating tank class, a 5 step post-processing is used based on a Density-Based Spatial Clustering of Applications with Noise algorithm (DBCAN) after which blast simulation is applied and effects on people, buildings and trees are obtained through 4 steps. Finally, the petrochemical site of LaemChabang in Thailand is used as study case. Except for the road class that is difficult to detect, land cover is well performed. Human casualties and surface of damaged buildings are finally estimated demonstrating the usefulness of the tool to be used for the emergency planning of industrial disasters.     

Research on the prediction model of hazardous chemical road transportation accidents

In recent years, hazardous chemicals road transport accidents have occurred frequently, causing huge casualties and property losses, and accident risk assessment has become the focus of researchers' research. To predict the risk probability value of hazardous chemical road transport accidents, first, we compiled data on road transportation accidents of hazardous chemicals in China in the past five years. And the nine nodes in the Bayesian network (BN) structure were defined in combination with relevant classification standards. The optimal Bayesian network structure for hazardous chemical road transport accidents was determined based on the K2 algorithm and the causalities between the nodes. Second, the node conditional probabilities were derived by parameter learning of the model using Netica, and the validity of the model was verified using the 5-fold cross-validation method. Last, the Bayesian network model of hazardous chemical road transport accidents is used to analyze accident examples, and the accident chain of “rear-end-leakage” is predicted, and the accident is most likely to be disposed of within 3–9 h. The study shows that the derived accident prediction model for hazardous chemical road transportation can reason reasonably about the evolution of accident scenarios and determine the probability values of accident risks under different parameter conditions.     

基于BP神经网络的义水河水环境质量评价研究

水质质量涉及多个影响因子,因子间大多具有非线性相关性,因此,水质评价是一个具有多因子的非线性系统。人工神经网络具有非线性映射的特点,能够有效地处理非线性相关问题,将其应用于水质评价中能够获得更为准确的信息。文章以溶解氧、高锰酸盐指数、化学需氧量、氨氮、总磷、石油类等6个指标作为评价因子,通过构造BP网络模型对义水河水质进行评价,分析了义水河4个断面的水质状况,并与单因子评价法结果进行了比较分析。     

基于生态服务的城乡景观生态安全格局的构建

以安徽淮北市为例,针对城市发展所造成的城乡生态环境品质降低等问题,期望通过景观生态安全格局的构建,维护区域生态系统健康与稳定。文章基于生态系统服务和景观生态安全格局理论,利用生态服务功能价值评价以及耗费距离模型等分析方法,通过模拟重要生态廊道,与现有廊道进行对比,发现问题并提出优化策略,优化城市景观结构与功能,改善城乡生态系统服务。研究结果表明,淮北城市森林在空间上的衔接性不高;研究区域内众多湿地没有足够的廊道相连,尚未形成良好的湿地生态网络;主城区交通廊道密度较高,但连接度不够,乡村地区交通廊道分布较少且稀疏。因而进行城乡景观生态安全格局的构建十分重要和必要。研究结果可为淮北生态城市建设提供一定的参考,为其他相关研究提供思路和方法上的借鉴。     

基于地理分区及神经网络的湖泊水库富营养化研究

构建了一个基于地理分区及神经网络的湖泊水库富营养化综合评价体系.以美国环境保护署Nutrient Criteria Database数据库为参照对象,有针对性地研究我国湖泊水库的情况,首次提出了基于地理分区的简易评价标准,对不同地理特征的水体富营养化临界值进行了定量研究.同时本研究还建立了基于神经网络的富营养化评价模型...     

SVM与ANN在湖泊富营养化评价中的对比研究

支持向量机是由Vapnik等提出的建立在统计学习理论基础上的一种新的机器学习方法,由于其使用结构风险最小化原则代替经验风险最小化原则,又由于其应用了核函数思想,它可以较好地解决非线性问题;人工神经网络(ANN)已经较成功解决模式识别和任意非线性函数回归问题,但是存在训练样本不足,并可能出现过拟合现象。SVM的结构风险最小化算法引起了科学界的关注,对传统基于经验风险最小化的神经网络算法提出了挑战,文章介绍了SVM和ANN的基本原理,并对二者在巢湖富营养化水平评价上做对比研究,结果表明,ANN比较容易陷入局部最优,支持向量机评价结果更加符合实际。     

基于LM-BP神经网络的Argo数据西北太平洋海水温度模型

以2007年西北太平洋海域Argo海表面温度、经纬度、深度为输入参数,利用LM-BP神经网络,构建了西北太平洋海水温度模型。将均方根差以及Pearson相关性系数作为检验指标,利用2008年和2009年的Argo数据对模型进行了检验。检验结果为:2008年均方根误差为0.714 0℃,Pearson相关性系数为0.996 8;2009年均方根误差为0.761 5℃,Pearson相关性系数为0.9965。表明所建立的基于LM-BP神经网络的Argo数据西北太平洋海水温度模型是可行的。