An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA

Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convolutional neural networks (CNN) is employed to extract the temporal dependence and spatial correlation of the input, while feature maps and channels are weighted by attention mechanism, so as to improve the effectiveness of the features. Finally, a depth long short term memory (LSTM) based time series predictor is established for learning the long-term and short-term dependence of pollutant concentration. In order to reduce the effect of diverse complex factors on LSTM, inherent features are extracted from historical air pollutant concentration data meteorological data and timestamp information are incorporated into the proposed model. Extensive experiments were performed using the Attention-CNNLSTM, autoregressive integrated moving average (ARIMA), support vector regression (SVR), traditional LSTM and CNN, respectively. The results demonstrated that the feasibility and practicability of Attention-CNN-LSTM on estimating CO and NO concentration.

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A hybrid model for short-term air pollutant concentration forecasting

2015 IEEE International Conference on Service Operations And Logistics, And Informatics (SOLI) ◽

10.1109/soli.2015.7367614 ◽

2015 ◽

Cited By ~ 3

Author(s):

Xin Zhang ◽

Xiaoguang Rui ◽

Xi Xia ◽

Xinxin Bai ◽

Wenjun Yin ◽

...

Keyword(s):

Hybrid Model ◽

Air Pollutant ◽

Pollutant Concentration ◽

Short Term

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An Ensemble Model for Short-Term Wind Power Forecasting using Deep Learning and Gradient Boosting Algorithms

2020 21st National Power Systems Conference (NPSC) ◽

10.1109/npsc49263.2020.9331902 ◽

2020 ◽

Author(s):

Devesh Kumar ◽

Rishabh Abhinav ◽

Naran Pindoriya

Keyword(s):

Deep Learning ◽

Wind Power ◽

Gradient Boosting ◽

Ensemble Model ◽

Short Term ◽

Wind Power Forecasting ◽

Boosting Algorithms ◽

Power Forecasting

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Spatio-temporal associations of air pollutant concentrations, GP respiratory consultations and respiratory inhaler prescriptions: a 5-year study of primary care in the borough of Lambeth, South London

Environmental Health ◽

10.1186/s12940-021-00730-1 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Mark Ashworth ◽

◽

Antonis Analitis ◽

David Whitney ◽

Evangelia Samoli ◽

...

Keyword(s):

Primary Care ◽

Negative Binomial ◽

Hospital Admissions ◽

Air Pollutant ◽

Respiratory Morbidity ◽

Short Term ◽

Short Term Exposure ◽

Short And Long Term ◽

Spatio Temporal

Abstract Background Although the associations of outdoor air pollution exposure with mortality and hospital admissions are well established, few previous studies have reported on primary care clinical and prescribing data. We assessed the associations of short and long-term pollutant exposures with General Practitioner respiratory consultations and inhaler prescriptions. Methods Daily primary care data, for 2009–2013, were obtained from Lambeth DataNet (LDN), an anonymised dataset containing coded data from all patients (1.2 million) registered at general practices in Lambeth, an inner-city south London borough. Counts of respiratory consultations and inhaler prescriptions by day and Lower Super Output Area (LSOA) of residence were constructed. We developed models for predicting daily PM2.5, PM10, NO2 and O3 per LSOA. We used spatio-temporal mixed effects zero inflated negative binomial models to investigate the simultaneous short- and long-term effects of exposure to pollutants on the number of events. Results The mean concentrations of NO2, PM10, PM2.5 and O3 over the study period were 50.7, 21.2, 15.6, and 49.9 μg/m3 respectively, with all pollutants except NO2 having much larger temporal rather than spatial variability. Following short-term exposure increases to PM10, NO2 and PM2.5 the number of consultations and inhaler prescriptions were found to increase, especially for PM10 exposure in children which was associated with increases in daily respiratory consultations of 3.4% and inhaler prescriptions of 0.8%, per PM10 interquartile range (IQR) increase. Associations further increased after adjustment for weekly average exposures, rising to 6.1 and 1.2%, respectively, for weekly average PM10 exposure. In contrast, a short-term increase in O3 exposure was associated with decreased number of respiratory consultations. No association was found between long-term exposures to PM10, PM2.5 and NO2 and number of respiratory consultations. Long-term exposure to NO2 was associated with an increase (8%) in preventer inhaler prescriptions only. Conclusions We found increases in the daily number of GP respiratory consultations and inhaler prescriptions following short-term increases in exposure to NO2, PM10 and PM2.5. These associations are more pronounced in children and persist for at least a week. The association with long term exposure to NO2 and preventer inhaler prescriptions indicates likely increased chronic respiratory morbidity.

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Phytoplankton Adaptation to Environmental Stresses from Toxicants, Nutrients, and Pollutants — a Warning

Journal of the Fisheries Research Board of Canada ◽

10.1139/f76-260 ◽

1976 ◽

Vol 33 (9) ◽

pp. 2089-2096 ◽

Cited By ~ 47

Author(s):

John G. Stockner ◽

Naval J. Antia

Keyword(s):

Environmental Protection ◽

Pollutant Concentration ◽

Subsequent Growth ◽

Short Term ◽

Present Evidence ◽

Test Chemical ◽

Successful Adaptation ◽

Shock Response ◽

Chronic Pollution

Examples are cited from the literature of phytoplankton-related pollution and nutrition studies where the possibility of successful adaptation and subsequent growth could have been overlooked because of insufficient duration of algal exposure to the pollutant or nutrient tested. We present evidence from our investigations where: a) initial algal exposures as long as 20–40 days to the pollutant or alternative nutrient may be required for successful adaptation, and b) phytoplankters initially tolerating only a low level of pollutant concentration could be trained to accept severalfold higher levels by repeated exposure to gradually increasing pollutant concentration A plea is made for future investigators to recognize the importance of long-term bioassays ascertaining algal potential for adaptation, in order that their results may be ecologically realistic for the purpose of environmental protection against chronic pollution and eutrophication. The short-term "shock" response should be clearly distinguished from the long-term habituation response of phytoplankters to the test chemical in these bioassays. Possible problems raising questionable objections to the long-term bioassay approach are discussed.

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An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Energies ◽

10.3390/en14113020 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3020

Author(s):

Anam-Nawaz Khan ◽

Naeem Iqbal ◽

Atif Rizwan ◽

Rashid Ahmad ◽

Do-Hyeun Kim

Keyword(s):

Cluster Analysis ◽

Deep Learning ◽

Residential Buildings ◽

Ensemble Forecasting ◽

Forecasting Model ◽

Ensemble Model ◽

Learning Models ◽

Short Term ◽

Floor Level ◽

Consumption Data

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

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