scholarly journals Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
State Equation ◽  
Ar Model ◽  
Prediction Algorithm ◽  
Time Series Models ◽  
Multiple Model ◽  
Concentration Data ◽  
Interactive Multiple Model

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5is the main particulate matter in air pollution. Therefore, how to predict PM2.5accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5prediction, and it is effective.

scholarly journals Evaluation of multivariate time series clustering for imputation of air pollution data

2021 ◽  
Vol 10 (2) ◽  
pp. 265-285
Author(s):  
Wedad Alahamade ◽  
Iain Lake ◽  
Claire E. Reeves ◽  
Beatriz De La Iglesia
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Missing Data ◽  
Multivariate Time Series ◽  
Real Data ◽  
Economic Benefits ◽  
Air Pollutant ◽  
Monitoring Site ◽  
Concentration Data

Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air-pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However, problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a multivariate time series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutant concentrations produces very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours and of the characteristics of different stations according to their environmental type.

scholarly journals Evaluation of Multi-variate Time Series Clustering for Imputation of Air Pollution Data

2021 ◽  
Author(s):  
Wedad Alahamade ◽  
Iain Lake ◽  
Claire E. Reeves ◽  
Beatriz De La Iglesia
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Missing Data ◽  
Multivariate Time Series ◽  
Real Data ◽  
Economic Benefits ◽  
Air Pollutant ◽  
Monitoring Site ◽  
Concentration Data

Abstract. Air pollution is one of the world's leading risk factors for death, with 6.5 million deaths per year worldwide attributed to air pollution-related diseases. Understanding the behaviour of certain pollutants through air quality assessment can produce improvements in air quality management that will translate to health and economic benefits. However problems with missing data and uncertainty hinder that assessment. We are motivated by the need to enhance the air pollution data available. We focus on the problem of missing air pollutant concentration data either because a limited set of pollutants is measured at a monitoring site or because an instrument is not operating, so a particular pollutant is not measured for a period of time. In our previous work, we have proposed models which can impute a whole missing time series to enhance air quality monitoring. Some of these models are based on a Multivariate Time Series (MVTS) clustering method. Here, we apply our method to real data and show how different graphical and statistical model evaluation functions enable us to select the imputation model that produces the most plausible imputations. We then compare the Daily Air Quality Index (DAQI) values obtained after imputation with observed values incorporating missing data. Our results show that using an ensemble model that aggregates the spatial similarity obtained by the geographical correlation between monitoring stations and the fused temporal similarity between pollutants concentrations produced very good imputation results. Furthermore, the analysis enhances understanding of the different pollutant behaviours, and of the characteristics of different stations according to their environmental type.

scholarly journals Improved Semiparametric Time Series Models of Air Pollution and Mortality

2004 ◽  
Vol 99 (468) ◽  
pp. 938-948 ◽  
Author(s):  
Francesca Dominici ◽  
Aidan McDermott ◽  
Trevor J Hastie
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Series Models

Air quality management using genetic algorithm based heuristic fuzzy time series model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lalit Bhagat ◽  
Gunjan Goyal ◽  
Dinesh C.S. Bisht ◽  
Mangey Ram ◽  
Yigit Kazancoglu
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Quality Management ◽  
Fuzzy Time Series ◽  
Selection Algorithm ◽  
Content Type ◽  
Proposed Model ◽  
Adaptive Time

PurposeThe purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.Design/methodology/approachIn this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.FindingsThe proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.Practical implicationsThe management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.Originality/valueThe proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

Seasonal drought forecasting in arid regions, using different time series models and RDI index

2019 ◽  
Vol 11 (3) ◽  
pp. 633-654 ◽  
Author(s):  
Mohammad Mehdi Moghimi ◽  
Abdol Rassoul Zarei ◽  
Mohammad Reza Mahmoudi
Keyword(s):  
Time Series ◽  
Ar Model ◽  
Time Series Models ◽  
Climatic Data ◽  
Drought Forecasting ◽  
Seasonal Drought ◽  
Drought Assessment ◽  
Model Based ◽  
Modeling And Forecasting ◽  
Drought Classes

Abstract Confronting drought and reducing its impacts requires modeling and forecasting of this phenomenon. In this research, the ability of different time series models (the ARIMA models with different structures) were evaluated to model and predict seasonal drought based on the RDI drought index in the south of Iran. For this purpose, the climatic data of 16 synoptic stations from 1980 to 2010 were used. Evaluation of time series models was based on trial and error. Results showed drought classes varied between ‘very wet’ to ‘severely dry’. The more occurrence frequency of ‘severely dry’ class compared to other drought classes represent the necessity of drought assessment and the importance of managing the effects of this phenomenon in the study area. Results showed that the highest severity of drought occurred at Abadeh, Shiraz, Fasa, Sirjan, Kerman, Shahre Babak and Saravan stations. According to selecting the best model fitted to the computed three-month RDI time series, results indicated that the MA model based on the Innovations method resulted in maximum cases with the best performance (37.5% of cases). The AR model based on the Yule–Walker method resulted in minimum cases with the best performance (6.3% of cases) in seasonal drought forecasting.

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

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