基于KNN-LSTM的PM2.5浓度预测模型

目前多数PM_2.5浓度预测模型仅利用单个站点的时间序列数据进行浓度预测, 并没有考虑到空气质量监测站之间的区域关联性, 这会导致预测存在一定的片面性. 本文利用KNN算法选择目标站点所在区域中与其相关的空间因素, 并结合LSTM模型, 提出基于时空特征的KNN-LSTM的PM_2.5浓度预测模型. 以哈尔滨市10个空气质量监测站的污染物数据进行仿真实验, 并将KNN-LSTM模型与其他预测模型进行对比, 结果显示: 模型相较于BP神经网络模型平均绝对误差(MAE)、均方根误差(RMSE)分别降低了19.25%、13.23%; 相较于LSTM模型MAE、RMSE分别降低了4.29%、6.99%. 表明本文所提KNN-LSTM模型能有效提高LSTM模型的预测精度.     

环境空气SO2和NO2浓度的GA_ANN预测模型研究

空气中污染物浓度的预测是一个复杂的非线性问题。国内外的研究表明神经网络能够比回归模型更好地预报空气污染物。设计并实现了将用于选择最优预报因子的遗传算法和神经网络算法相结合的GA_ANN空气质量预测模型,利用某市2003~2006年的数据建立神经网络空气质量预测模型,对该市2007年全年SO₂和NO₂的预测实验表明,GA_ANN模型比单纯的神经网络模型具有更高的预报精度。     

基于模糊时序和支持向量机的高速公路SO2浓度预测算法

针对现有SO₂浓度预测方法中存在的污染物来源和影响因素认识不统一、小样本数据敏感、易于陷入局部最优等问题,文中提出了基于模糊时序和支持向量机的高速公路SO₂浓度预测算法,为搭建高速公路环境健康监测系统提供了可靠的理论支持.该方法依据SO₂浓度的季节变动规律,以季节作为时间序列,以24h为粒化窗宽,通过高斯核函数提取原始样本数据的特征值,输入支持向量机训练模型,并利用k重交叉验证法结合网格划分优化模型参数.文中应用该方法建立了SO₂浓度预测模型,并以2014年4月至2015年3月山西省太旧高速公路某监测点SO₂小时浓度监测值为样本数据,在MATLAB平台下应用LIBSVM工具实现了计算过程.结果表明,基于模糊时序和支持向量机的高速公路SO₂浓度预测算法不受机理性理论研究的限制,支持小样本学习,非线性拟合效果好,泛化能力强.     

广义加权最小二乘组合预测法在装备故障率预测中的应用

为了准确预测武器装备的故障率,针对实验数据少,随机因素影响较大的特点,在最小二乘法的基础上,构建广义加权组合预测模型,把最小二乘法与加权组合预测法有机的结合起来.在Matlab环境下,通过最小二乘法确立多个单项预测模型,经过广义加权组合法综合不同模型的信息,利用非线性规划法求解最优权系数.通过实际数例的仿真计算,证明了广义加权最小二乘组合预测模型能有效的降低预测误差,提高预测精度.     

基于支持向量机的烟气二氧化硫排放量预测模型

针对循环流化床锅炉控制系统的烟气SO₂对象的非线性特点,本文建立了一种基于支持向量机的烟气SO₂排放量预测模型. 由于直接网格搜索确定支持向量机回归模型参数的方法计算量大、搜索时间长,本文采用单变量参数搜索结合网格寻优的方法来确定模型参数. 仿真结果表明,基于支持向量机方法建立的循环流化床锅炉烟气SO₂排放量预测模型具有良好的预测效果.     

基于注意力机制和高斯概率估计的PM2.5浓度预测

作为衡量空气污染物浓度的重要指标, 对PM_2.5浓度进行监控预测, 能够有效地保护大气环境, 进一步地减少空气污染带来的危害. 随着空气质量自动监测站的大范围建立, 由传统的机器学习搭建的空气质量预测模型已经不能满足当今的需求. 本文提出了一种基于多头注意力机制和高斯概率估计的高斯-注意力预测模型, 并对沈阳市某监测站点的数据进行了训练和测试. 该模型考虑了PM_2.5浓度受到其他空气质量数据的影响, 将空气质量数据的分层时间戳(周、日、小时)的信息对齐作为输入, 使用多头注意力机制对于不同子空间的时间序列关联特征进行提取, 能够获得更加完善有效的特征信息, 再经过高斯似然估计得到预测结果. 通过与多种基准模型进行对比, 相较于性能较优的DeepAR, 高斯-注意力预测模型的MSE、MAE分别下降了21%、15%, 有效地提高了预测准确率, 能够较准确地预测出PM_2.5浓度.     

GA-ELM在硫铁矿制酸尾气SO2浓度预测的应用

基于对整个生产流程的管控,使硫铁矿生产硫酸尾气的SO₂浓度达标排放,提出运用GA-ELM对制酸尾气SO₂浓度进行建模预测.在硫铁矿制酸的生产过程中采集对尾气SO₂浓度影响较大的关键点参数,运用GA-ELM神经网络对烟气制酸尾气SO₂浓度进行预测.该方法在某厂实际检验,其预测结果与实际数据吻合度较高,对于调整和优化工艺指标和尾气达标排放起到很好的指导作用.     

基于GPRS/SMS空气污染远程监控系统的设计与实现

为了实现空气质量指数的智能监测和管理,设计并实现了一种基于SnO₂人工嗅觉探测的空气质量指数监测系统,系统通过空气质量指数现场网络监测节点采集、处理以及传递空气质量指数数据,采用ARM S3CA510B进行DTU数据传递,实现空气质量指数数据CO、SO₂等的高效率、大批量的传递;使用汇聚节点模块通过GPRS网络将数据传递到监管终端,监管终端将管理命令通过GPRS网络反馈到汇聚监测节点,实现空气质量指数数据的远程监测;采用监测管理终端为管理人员提供不同的监测处理、控制处理以及空气质量指数监测相关数据检索和汇总分析等人机界面;对系统空气质量指数信息流的传递流程图进行了设计,给出了系统数据通过串口进行通信的代码以及系统监管终端使用套接字实现网络通信的核心源码;实验结果说明,与传统系统相比,所提系统可实时监测出空气中CO、SO₂等污染气体的浓度,具有很高的实用性及有效性。     

高速公路交通量组合预测模型研究

交通量具有高度复杂的非线性特征,采用单一预测模型往往难以达到理想的预测效果.为准确预测,提出一种最优线性组合预测模型并给出了以预测误差平方和最小为目标函数的权系数最优解计算方法,在采用ARIMA模型、BP神经网络和支持向量回归机的基础上,利用组合预测模型实现了高速公路月度交通量的预测.实验结果表明:与季节差分自回归滑动平均模型、BP神经网络和支持向量回归机等预测模型相比,组合预测模型各项评价指标均优于前三者,为实现交通量准确预测提供了更为科学的依据.     

基于SSA-LSTM模型的日水位预测—以涡河流域涡阳闸为例

水位的准确预测可以指导城市的防洪减灾举措及水利工程建设, 提升城市洪涝灾害应急响应速度. 基于数据驱动的水位预测模型, 尤其是LSTM模型, 在模拟自然界中水文要素的强非线性关系时展现出优势从而得到广泛应用. 然而, 自然界中水文数据的采集往往伴随着噪声以及人为干扰因素, 这些问题影响了模型的预测性能. 针对这一问题, 本文开发了一种新的组合模型, 即SSA-LSTM模型. 该模型首先利用SSA方法将观测到的时间序列分解为周期、趋势和噪声分量, 接着利用LSTM对SSA方法去噪后的序列进行模型训练并得到最终预测结果.本文选取涡河流域涡阳闸1971年5月至2020年12月的闸上水位为数据集, 1)利用奇异谱分析方法将原始水位时序数据分解为多个趋势和噪声分量(RC₁–RC₁₂), 选取分量(RC₁–RC₁₀)为趋势项并重构为新的水位时序信号; 2)利用LSTM模型对重构的信号进行了训练和验证, 并将预测结果与LSTM模型的结果进行了对比; 3)为得到最优的SSA-LSTM模型, 针对不同的时间步长(7、14、21、28、35天)开展了单步预测性能评估实验, 实验结果表明, 在不同的时间步长下, SSA-LSTM水位预测模型的决定系数R²、均方根误差RMSE、平均绝对误差百分比MAPE均优于LSTM模型. 由此可见, 采用 SSA方法对涡阳闸水位的预处理可有效提高 LSTM 的预测效果, 相比于传统 LSTM 模型, SSA-LSTM模型具有高可靠和低误差的特点, 在水位预测应用中更具适应性, 可以为城市防洪、灌溉、供水等水利措施的合理调度提供更优的决策依据.     

A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight

Effective forecasting of the air pollutant concentration is crucial for a robust air quality early-warning system and has both theoretical and practical significance. However, the accidental and cognitive uncertainty in the model selection or parameter setting of a single system will result in inaccurate and unstable forecasting results. Thus, in this paper, a novel fuzzy combination forecasting system based on the data preprocessing, fuzzy theory, and advanced optimization algorithm is proposed to improve the accuracy and stability of forecasting results. Based on the fuzzy theory and decorrelation maximization method, our proposed forecasting system can considering more information and maintaining the diversity of models. Moreover, Cuckoo Search algorithm applied in the system can determine the optimal weights for models aggregation. Several experiments based on PM${}_{\mathrm{2.5}}$ and PM₁₀ datasets in three cities are analyzed and discussed to verify the excellent performance of our proposed forecasting system, and the results indicate that the forecasting system outperforms others with respect to the accuracy, stability and generalization capabilities which are the basis of a robust air quality early-warning system in practice.     

A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)

The forecasting of air pollution is important for living environment and public health. The prediction of SO₂ (sulfur dioxide), which is one of the indicators of air pollution, is a significant part of steps to be done in order to decrease the air pollution. In this study, a novel feature scaling method called neighbor-based feature scaling (NBFS) has been proposed and combined with artificial neural network (ANN) and adaptive network–based fuzzy inference system (ANFIS) prediction algorithms in order to predict the SO₂ concentration value is from air quality metrics belonging to Konya province in Turkey. This work consists of two stages. In the first stage, SO₂ concentration dataset has been scaled using neighbor-based feature scaling. In the second stage, ANN and ANFIS prediction algorithms have been used to forecast the SO₂ value of scaled SO₂ concentration dataset. SO₂ concentration dataset was obtained from Air Quality Statistics database of Turkish Statistical Institute. To constitute dataset, the mean values belonging to seasons of winter period have been used with the aim of watching the air pollution changes between dates of December, 1, 2003 and December, 30, 2005. In order to evaluate the performance of the proposed method, the performance measures including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and IA (Index of Agreement) values have been used. After NBFS method applied to SO₂ concentration dataset, the obtained RMSE and IA values are 83.87–0.27 (IA) and 93–0.33 (IA) using ANN and ANFIS, respectively. Without NBFS, the obtained RMSE and IA values are 85.31–0.25 (IA) and 117.71–0.29 (IA) using ANN and ANFIS, respectively. The obtained results have demonstrated that the proposed feature scaling method has been obtained very promising results in the prediction of SO₂ concentration values.     

正则化和交叉验证在组合预测模型中的应用

组合预测模型的权重确定方式对于提高模型精度至关重要,为研究正则化与交叉验证是否能改善组合预测模型的预测效果,提出将正则化和交叉验证应用于基于最小二乘法的组合预测模型.通过在组合模型的最优化求解中分别加入L1、L2范数正则化项,并对数据集进行留一交叉验证后发现:L1、L2范数正则化都对组合模型的预测精度具有改善效果,且L1范数正则化比L2范数正则化对组合预测模型的改善效果更好,并且参与组合预测的单项预测模型越多,正则化的改善效果越好,交叉验证对组合预测模型的改善效果则与给定实验数据量呈现正相关.     

Patterns of SO2 emissions: a refinery case study

Air quality modelling is an essential tool for most air pollution studies and the introduction of SO₂ standards creates a need for modelling the dispersion of SO₂. This work deals specifically with the use of the Industrial Source Complex Short Term (ISCST) model at a refinery. The study is performed over a period of 21 days. The first objective of this study was to measure the atmospheric levels of SO₂ and then to compare their values with the international standard limits. The second objective was to evaluate the ISCST model by comparing the calculated and measured concentrations. The third objective was to demonstrate the effect of wind regimes on the dispersion of SO₂ and to determine the spatial distribution of SO₂ over the modelled area. The results showed that the levels of SO₂ were well below the ambient air quality standard. Based on isopleths for SO₂ distribution in the study area (as output from the ISCST model), it can be stated that no health risk is present in areas adjacent to the refinery.     

基于GA-GRU环境空气污染物预测研究

针对现有的环境空气污染物预测方法存在缺少输入特征相关性分析和时序信息丢失问题,提出一种将遗传算法和门控循环单元神经网络相结合的环境空气污染物PM2.5小时浓度预测模型,充分挖掘了污染物时间序列内在依赖关系,并解决了不相关因子的干扰和输入特征维度灾难的问题。最后基于绵阳市4个空气污染物监测站点的数据集进行仿真实验,与门控循环单元神经网络、深度信念网络预测比较。结果表明,基于GA-GRU的PM2.5小时浓度预测模型在训练时间、预测精度和鲁棒性上优势显著,是一种可行且有效的预测方法。     

基于SFLA-CNN和LSTM组合模型的水位预测

水文时间序列受降雨量的影响,在变化规律上呈现季节性、非线性的特点.传统单一模型结构简单,对于复杂的非线性水文时间序列具有预测精度较低、不能很好捕捉水文时间序列的复合特征的问题.组合预测模型采用多分类器的思想,能够有效地提高预测准确度,然而在模型参数选择方面需要手工调参,花费时间多且不准确.本文提出一种基于SFLA-CN...     

Predicting daily ozone concentration maxima using fuzzy time series based on a two-stage linguistic partition method

Air pollution is a result of global warming, greenhouse effects, and acid rain. Especially in highly industrialization areas, air pollution has become a major environmental issue. Poor air quality has both acute and chronic effects on human health. The detrimental effects of ambient ozone on human health and the Earth’s ecosystem continue to be a national concern in Taiwan. The pollutant standard index (PSI) has been adopted to assess the degree of air pollution in Taiwan. The standardized daily air quality report provides a simple number on a scale of 0 to 500 related to the health effects of air quality levels. The report focuses on health and the current PSI subindices to reflect measured ozone (O₃) concentrations. Therefore, this study uses the O₃ attribute to evaluate air quality. In an effort to forecast daily maximum ozone concentrations, many researchers have developed daily ozone forecasting models. However, this continuing worldwide environmental problem suggests the need for more accurate models. This paper proposes two new fuzzy time series based on a two-stage linguistic partition method to predict air quality with daily maximum O₃ concentration: Stage 1, use the fuzzy time series based on the cumulative probability distribution approach (CPDA) to partition the universe of discourse into seven intervals; Stage 2, use two linguistic partition methods, the CPDA and the uniform discretion method (UDM), to repartition each interval into three subintervals. To verify the forecasting performance of the proposed methods in detail, the practical collected data is used as and evaluating dataset; five other methodologies (AR, MA, ARMA, Chen’s and Yu’s) are used as comparison models. The proposed methods both show a greatly improved performance in daily maximal ozone concentration prediction accuracy compared with the other models.