桑叶表面氟化物吸附积累规律的统计研究

以各气象因素和大气氟化物浓度为生态因子,对大田桑园中各叶位桑叶的氟化物的吸附积累规律,进行了统计分析和研究,建立了各叶位桑叶的氟化物的吸附积累模型。     

大气污染事故预测系统的研究

将地理信息系统应用于城市突发性的大气污染事故，对大气扩散模型的图形技术进行开发研究。介绍了设计的总体结构；详细叙述了数学模型的建立、编程语言及其关键技术。该技术能模拟显示化学品泄漏后的大气污染扩散范围，为应急反应科学决策服务。     

日本大气污染和控制技术研究进展

本文详细地介绍了日本大气污染现状及其趋势,对主要污染物的污染状况进行了分析。概述了日本几十年来大气污染防治技术研究的进展。对改善燃烧、电子束法、NO_x处理触媒研究、NO_x吸附剂的研究、活性炭法干式脱硫、利用煤炭的干式脱硫、半干式简易脱硫、静电的应用、微生物的应用、电除尘、过滤除尘等大气污染防治技术的研究,做了详细介绍。对目前人们普遍关心的含氟气体排放控制问题进行了探讨。还简要介绍了日本大阪府等地,准备偿试的汽车排气总量控制的研究状况。     

大气环境质量的人工神经网络决策模型

为了对环境质量进行综合评价,运用误差反向传播算法的人工神经网络方法建立了环境质量评价的Ｂ－Ｐ决策模型。用此模型研究实例的大气环境质量,结果表明Ｂ－Ｐ网络用于环境质量评价具有客观性和实用性。     

基于结构化设计的湖、库富营养化模型研究

基于结构化设计概念，将LEEDs模型与人工神经网络模型耦合，建立湖、库富营养化模型，模型包含N、P、COD、水温、水深和流速等6个控制模块。并以三峡水库为例预测结果表明，在3月和9～10月，月平均气温约为17℃，水深约为5m，流速约为0．02m／s时，三峡库区水体富营养化指标最大。模型具有可扩展性强，维护、升级容易等优点，可对模型中各个相对独立的模块进行调整，以适应不同湖库的实际情况。     

土壤中重金属累积预测模型研究进展

健康的土壤是维持人体健康以及国家粮食安全的重要保障.中国部分地区土壤重金属污染较为严重,因此采用合理的预测模型对土壤中的重金属污染状况进行分析和预测,对于土壤的健康管理具有重要意义.聚焦土壤中重金属累积预测模型研究现状,简要分析土壤中重金属的累积行为,阐述不同重金属累积预测模型原理以及实际应用,总结影响预测模型准确度的...     

基于改进支持向量回归机的污水处理厂出水总氮预测模型

在小样本数据的情况下,采用粒子群优化算法(PSO)对传统支持向量回归机(SVR)进行改进,将其应用于北京某大型污水处理厂出水总氮浓度预测上。 预测结果精度对比分析表明,PSO-SVR模型预测结果平均相对误差为1.836%,决定系数为67.76%,均方根误差为0.693 9,各评价指标均优于多元线性回归模型、BP神经网络模型。因此在小样本情况下,利用PSO-SVR模型对污水处理厂出水总氮浓度进行预测是可行有效的,为应用数据驱动模型对污水处理过程进行建模模拟提供了一种新方法尝试。     

多元回归和BP人工神经网络在预测混合厌氧消化产气量过程中的应用比较

对脂肪类单基质和城市污水厂剩余污泥混合厌氧消化过程的产气阶段进行基于多元回归和BP人工神经网络的产气量预测模型比较研究。实验数据分别取自反应过程的第1～16天和第17～70天。结果表明:多元回归模型的预测平均准确率分别为75.69%和79.29%;BP神经网络模型的预测平均准确率为79.05%。通过对比2种模型的预测结果可知,两种模型都有较高的预测准确率,但BP模型的预测准确率更高,更适用于混合厌氧消化产气量预测。     

基于径向基函数网络的溢油预测模型

为了提高溢油预测的准确性,建立和优化溢油预测模型,提出了基于径向基函数网络模型的溢油预测方法,实现溢油预测功能.径向基函数网络模型解决了模拟预测过程中样本库巨大、函数模型收敛速度慢的问题.通过选择有效的输入参数和样本数据,建立局部逼近网络;通过径向基函数训练样本数据,利用输出值与实际值之间的误差作为约束条件调整权重因子、径向基中心和宽度,加快函数模型的收敛速度.该模型模拟了溢油的漂移、扩散过程,达到预测的目的.利用该模型,建立了溢油预测模块,并针对一次溢油事故进行预测模拟,验证了该模型的可行性,能够为应急决策提供一定的支持.     

基于遗传算法的大气污染总量控制新方法

提出一种利用遗传算法进行大气污染总量控制的新方法,并以柳州市SO2总量控制为例说明这种方法的具体运用.遗传算法具备自适应全局搜索寻优特点,从控制点浓度推算源强分布,该源强分布满足总量控制的根本要求.该方法的具体实现是:将区域内各污染源的排放量编码为染色体,让染色体群体在模拟的进化环境下按生物进化规律进行优胜劣汰的自然选择,经过若干代的进化,最终得到的最优个体即代表最佳的源强分布.研究结果表明,这种方法是有效的和可行的.     

基于人工神经网络的街道峡谷NOx浓度的数值模型研究

通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1．11,训练样本的模拟值与实测值的相关系数为0．93,测试样本的模拟值与实测值的相关系数为0．87,模拟值与实测值的相关系数均高于显著水平为α=0．05与α=0．01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。     

Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting

Multi-layer perceptron (MLP) artificial neural network (ANN) models are compared with traditional multiple regression (MLR) models for daily maximum and average O3 and particulate matter (PM10 and PM2.5) forecasting. MLP particulate forecasting models show little if any improvement over MLR models and exhibit less skill than do O3 forecasting models. Meteorological variables (precipitation, wind, and temperature), persistence, and co-pollutant data are shown to be useful PM predictors. If MLP approaches are adopted for PM forecasting, training methods that improve extreme value prediction are recommended.     

Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels

Environmental Science and Pollution Research - The dilemma between health concerns and the economy is apparent in the context of strategic decision making during the pandemic. In particular,...     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

基于人工神经网络的街道峡谷NO_x浓度的数值模型研究

通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1.11,训练样本的模拟值与实测值的相关系数为0.93,测试样本的模拟值与实测值的相关系数为0.87,模拟值与实测值的相关系数均高于显著水平为α=0.05与α=0.01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。     

A cumulative-risk assessment method based on an artificial neural network model for the water environment

Environmental Science and Pollution Research - To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was...     

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City,China

Primary fine particulate matters with a diameter of less than 10 µm (PM₁₀) are important air emissions causing human health damage. PM₁₀ concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM₁₀ pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM₁₀ yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM₁₀ yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM₁₀ from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM₁₀ pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.

Implications The PM₁₀ (particulate matter with an aerodynamic diameter ≤10 μm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m³, was higher than the 65 μg/m³ recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM₁₀ yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM₁₀ spatial distribution. The results indicated that the artificial neural network was an effective method for PM₁₀ pollution modeling.     

Wavelet transform-based artificial neural networks (WT-ANN) in PM10 pollution level estimation, based on circular variables

Introduction  

In this paper, a novel method in the estimation and prediction of PM₁₀ is introduced using wavelet transform-based artificial neural networks (WT-ANN).     

Studies of air quality predictors based on neural networks

In recent years, urban air pollution has emerged as an acute problem because of its negative effect on health and living conditions. Regional air quality problems, in general, are linked to violations of specified air quality standards. The current study aims to find neural network based air quality predictors, which can work with a limited number of datasets and are robust enough to handle data with noise and errors. A number of available variations of neural network models, such as the Recurrent Network Model (RNM), the Change Point Detection Model with RNM (CPDM), the Sequential Network Construction Model (SNCM), the Self Organising Feature Model (SOFM), and the Moving Window Model (MWM), were implemented using MATLAB software for predicting air quality. Developed models were run to simulate and forecast based on the annual average data for 15 years from 1985 to 1999 for seven parameters, viz. VOC, NO_x, CO, SO₂, PM₁₀, PM_2.5 and NH₃ for one county of California, USA. The models were fitted with first nine years of data to predict data for remaining six years. The models, in general, could predict air quality patterns with modest accuracy. However, the SOFM model performed extremely well in comparison with the other models for predicting long-term (annual) data.