高层建筑电气火灾隐患因子定量分析方法研究

为了对高层建筑电气火灾隐患排查进行定量化分析,采用解释结构模型构建高层建筑电气火灾隐患因子的复杂逻辑模型;在利用德尔菲法确定隐患因子初始概率的基础上,经过交叉影响分析法求解交叉影响概率矩阵;引入马尔科夫链修正各隐患因子交叉影响后的概率,得到火灾隐患因子的稳态概率矩阵。研究结果表明:各隐患因子的马尔科夫链校正概率比德尔菲法测定的初始概率更加准确,该计算排序结果可明确高层电气火灾隐患排查和管控的重点。     

基于T-P模型的高低温环境型煤对甲烷吸附性能预测

为预测深部或浅部煤层不同温度和不同压力条件下的吸附等温线,选用型煤以高低温试验装置为依托,测试了温度为293.15,273.15,253.15 K的吸附等温线。基于T-P模型,利用等温吸附曲线对公式中的参数进行了合理的求解,探讨了一种简单的煤对瓦斯吸附等温线预测方法。研究表明:同一吸附平衡压力下,温度越低,煤的瓦斯吸附量越大;ε－ω吸附特征曲线与温度无关,呈现对数的形式;参数m和拟合度R2满足抛物线的关系,存在拟合效果最好时的参数m值。采用T-P模型预测得到的吸附等温线与实测的吸附等温线无论是趋势还是定量结果均十分吻合,其相对误差不超过5%。     

易自燃厚煤层工作面自然发火CO预测及防治

为了有效预防遗煤自燃,深入研究自然发火初期的CO预测技术。基于回风隅角CO源的理论模型,以Gambit建立相似二维采场模型,数值模拟了采空区自燃“三带”范围,并采用现场束管监测手段对结果进行了验证。利用程序升温实验获得了不同温度段回风隅角CO的极限指标,并与现场实测值对比分析,进而预判采空区遗煤发火程度,为制定有针对性的防治措施提供理论指导。研究结果表明:CO作为低温氧化阶段预测指标对预防遗煤自燃具有重要作用。     

安徽省经济发展与生态环境耦合协调评价与趋势预测

本文遵循客观、科学、综合原则,运用熵值赋权法,对经济与生态环境系统进行了评价;采用动态耦合模型及剪刀差分析方法对经济发展与生态环境系统耦合协调状况进行了考察;借助Excel 2003软件,通过作散点图,比较拟合优度分析方法,对两者的协调前景进行了预测。结果表明:①经济发展与生态环境系统综合指数分别由2005年的0.2934、0.4198跃升至2015年的0.6236、0.4198,经济发展系统升幅明显高于生态环境系统。②2005—2015年,经济发展与生态环境系统动态耦合度均值为-6.95°,整体处于由低级协调向协调演进状态,而两者间演化速度夹角由2005年的18.56°扩展至2015年的82.4°。③若按既有惯性发展模式,2020年经济发展与生态环境系统耦合度将达155.74°,预期处于极限发展状态。基于研究结果,本文提出了相应的应对策略,可为新常态下安徽省制定经济与生态文明融动发展政策提供决策参考。     

投影寻踪回归技术在环境污染预测中的应用

环境污染预测中常用的统计预测法基于“假定—模拟—预测”这样一种证实性数据分析思维模式,难以适应非线性、非正态分布数据分析。投影寻踪技术采用“审视数据—模拟—预测”这样一条探索性数据分析新途径,可以用来解决一定程度上的非线性、非正态的问题。应用投影寻踪回归新技术,建立了大气污染物SO₂浓度分类预测模型,其预测建模样本的拟合合格率为84.7％,预留预测检验样本预测合格率75％。     

汽车排放污染物浓度的预测

为了预测汽车排放污染物的浓度,应用简化的高斯烟团模式得到静风条件下的线源扩散预测模式。并结合高斯烟流扩散模式,建立了预测汽车污染物在任意风向下和年平均浓度的预测模式,考虑车道上存在车辆行驶的强烈机械扰动湍流和把繁忙的公路视为线源两个因素,提出了计算初始扩散参数的方法。然后,运用Turner和Pasquill扩散参数,建立了线源扩散参数的确定方法。该模式应用于预测高速公路沿途汽车污染物的浓度表明,计算值与监测值吻合较好,可用于我国公路环境影响的评价。     

二维多箱模型预测大气环境方法的研究

采用二维多箱模型对石家庄市区大气污染物ＳＯ２进行了计算,分别计算了４个风向的各子箱污染物浓度,然后按其风向频率加权取和得到平均浓度。把计算结果与地面监测值和１９９４－０１中国环境科学研究院的高空航测值进行了比较分析后发现。多箱模型预测大气环境比单箱模型和其它预测方法产生的误差要小。     

Evaluation of the effect of meteorological data resolution on Lagrangian particle dispersion simulations using the ETEX experiment

This paper presents results from a series of numerical experiments designed to evaluate operational long-range dispersion model simulations, and to investigate the effect of different temporal and spatial resolution of meteorological data from numerical weather prediction models on these simulations. Results of Lagrangian particle dispersion simulations of the first tracer release of the European Tracer Experiment (ETEX) are presented and compared with measured tracer concentrations. The use of analyzed data of higher resolution from the European Center for Medium-Range Weather Forecasts (ECMWF) model produced significantly better agreement between the concentrations predicted with the dispersion model and the ETEX measurements than the use of lower resolution Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast data. Numerical experiments were performed in which the ECMWF model data with lower vertical resolution (4 instead of 7 levels below 500 mb), lower temporal resolution (12 h instead of 6 h intervals), and lower horizontal resolution (2.5° instead of 0.5°) were used. Degrading the horizontal or temporal resolution of the ECMWF data resulted in decreased accuracy of the dispersion simulations. These results indicate that flow features resolved by the numerical weather prediction model data at approximately 45 km horizontal grid spacing and 6 h time intervals, but not resolved at 225 km spacing and 12 h intervals, made an important contribution to the long-range dispersion.     

Forecasting the impact of meteorological parameters on air pollutants in Andhra Pradesh using machine learning techniques

In the 21st century, air pollution has emerged as a significant problem all over the globe due to a variety of activities carried out by humans, such as the acceleration of industrialization and urbanization. SO₂, NO₂, and NH₃ are the key components contributing to air pollution. Moreover, these air pollutants have a significant connection to several climatic characteristics, such as the speed of the wind, the relative humidity, the temperature, the amount of precipitation, and the surface pressure. As a result, machine learning (ML) is regarded as a more effective strategy for predicting air quality than more conventional approaches such as probability and statistics, among others. In the research, Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), and Multi-Linear Regression (MLR) algorithms are used to make predictions about air quality, and MSE (Mean Squared Error), RMSE (Root Mean Square Error), MAE (Mean Squared error), and R² are used to determine how accurate the predictions are.     

边坡稳定性自动机器学习预测方法研究

为了简便有效地评估边坡稳定性状态,针对目前传统机器学习的算法选择与超参数优化等难题,提出1种边坡稳定性自动机器学习预测方法。首先,简要介绍5种主流开源自动机器学习框架;其次,以422组边坡稳定性样本为数据集,进行自动机器学习纯自动化训练,并与传统机器学习对比分析模型的性能与耗时;最后,综合讨论与比较典型自动机器学习框架的特性。研究结果表明：自动机器学习预测效果总体上优于传统机器学习,提升边坡稳定性预测准确率和稳健性,且无需人为干预。研究结果可为岩土工作人员准确可靠地评价边坡稳定性提供便捷条件。