基于LS-SVR、BP-ANN和MLR模型的PM10浓度预测

利用宁东能源化工基地PM10和气象监测数据,分别采用LS-SVR、BP-ANN和传统MLR模型预测PM10浓度变化。结果表明,较BP-ANN模型、MLR模型,LS-SVR模型能更好地刻画PM10浓度与各气象因素间的非线性相依关系,更准确地预测PM10浓度。     

大数据解析技术在大气环境监测中的应用研究

针对近年来新兴的大数据及挖掘、分析技术,对大数据解析技术在环境科学研究中的应用进行了分析和探析。以城市局部地区大气细颗粒污染物PM2.5浓度计算为例,筛选了PM2.5浓度历史数据、气象条件、交通状况、人群活动情况、网格道路状况等数据为影响特征量,分析了用神经网络法进行大数据解析和推演的原理和数学过程,是用数学、计算机、统计等方法研究解决多元、非线性复杂环境问题的一种新的探索。     

道路交通噪声预测声源简化研究

为了分析《环境影响评价技术导则声环境》(HJ 2.4—2009)中将道路声源简化为1条位于道路中心线处的线声源与按照车道数简化为多条线声源之间的误差,针对不同宽度的道路,推导了多条线声源与1条线声源在接收点噪声影响的误差计算公式,并基于Predictor-lima预测软件预测和现场噪声衰减规律实测进行了验证。研究结果表明,对于接收点到道路边缘的距离大于道路宽度的情况,可简化为1条线声源;对于接收点到道路边缘的距离小于道路宽度的情况,应按照车道数简化为多条线声源。     

基于改进的非线性GM(1,1)模型的职业病预测研究

为了更准确地预测职业病,在传统GM(1,1)模型的基础上,提出改进的非线性GM(1,1)模型,在传统的GM(1,1)模型中引入弱化算子,将紧邻均值与原始数据之间的线性假设改为非线性假设,提高曲线的拟合度。以2005—2014年的全国职业病例数为研究对象,进行数据拟合和预测分析,其中以2014年职业病例数作为验证数据,并利用后验差比值和小误差概率2个参数,检验该改进模型的预测精度。由应用实例的分析结果可知:在职业病发病趋势的预测方面,改进的非线性GM(1,1)模型的预测精度提高到一级,曲线拟合度较高,预测得到2015年的职业病例数为34 900例。     

不同自燃倾向性煤的指标气体产生规律实验研究

为研究不同自燃倾向性煤的自燃指标气体变化规律,提高对煤早期自燃预测预报的准确度,采用程序升温实验系统,得到内蒙古褐煤、神东长焰煤、河南气煤及枣庄焦煤4种不同变质程度煤的氧化时间随温度的变化关系,以及指标气体浓度在煤氧化过程中的变化规律。结果表明:自燃倾向性最高的褐煤应以CO和乙烯作为煤自燃早期预报的首选指标气体;易自燃的长焰煤应采用乙烯和烯烷比为主、以CO为辅的煤自燃判定指标;自燃倾向性较低的气煤应以乙烯和烯烷比作为煤自燃预报指标;CO是自燃倾向性最低的焦煤的最佳自燃预报指标气体。     

电厂建设运行中的水土流失预测与水土保持设计

在燃煤电厂建设运行的同时,将引起一系列的水土流失问题.以四川国电金堂电厂二期扩建工程为例,简要说明项目区水土流失预测的内容和方法,并在其基础上对可能产生的水土流失和危害进行了分析,同时介绍了建设期和运行期采取的综合防治措施,为类似工程的水土保持提供借鉴.     

天堂河河道治理工程水质改善预测分析

河道治理工程重在改善河道的水质,以恢复河流生态系统。本文以北京市大兴区天堂河河道治理工程为例,介绍了河道治理工程环境影响评价中的水质改善预测分析,首先分析生态需水量和供氧量是否满足河流生态需水要求,后又利用地面水环评助手软件预测了河流水质,预测分析了天堂河水质目标的可达性,旨在为以后的河道治理类工程环境影响评价提供参考。     

一种基于预测法的电磁兼容设计方法

文章以数模混合电路设计实践为基础,介绍了一种基于预测法的电磁兼容设计方法。先以电磁兼容测试理论为基础介绍数模混合电路设计中的电磁兼容方法,在以此方法建立电磁兼容设计模型,并通数模混合电路实践来检验和改进设计模型。     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model1

Moore, R.D. (Dan), J.W. Trubilowicz, and J.M. Buttle, 2011. Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model. Journal of the American Water Resources Association (JAWRA) 48(1): 32‐42. DOI: 10.1111/j.1752‐1688.2011.00595.x Abstract: Prediction of streamflow in ungauged basins is a global challenge, but is particularly an issue in physiographically complex regions like British Columbia (BC), Canada. The objective of this study was to assess the accuracy of a simple water balance model that can be run using existing spatial datasets. The model was developed by modifying an existing monthly water balance model to account for interception loss from forest canopy, glacier melt, and evaporation from lakes. The model was run using monthly climate normals from the ClimateBC application, which have a horizontal resolution of 400 m. Each ClimateBC grid cell was classified as forest, open land, glacier or water surface based on provincial scale digital maps of biogeoclimatic zones, glaciers, and water. The output was monthly mean runoff from each grid cell. These values were integrated within the catchment boundaries for streams gauged by the Water Survey of Canada. Annual runoff was predicted with modest accuracy: after updating the predicted runoff by interpolating errors from neighboring gauged streams, the mean absolute error was 25.4% of the gauged value, and 52% of the streams had errors less than 20%. However, the model appears to be quite robust in distinguishing between pluvial, hybrid, and melt‐dominated hydroclimatic regimes, and therefore has promise as a tool for catchment classification.