Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: A deep learning approach

Samal, K. Krishna Rani; Babu, Korra Sathya; Das, Santos Kumar

doi:10.1016/j.uclim.2021.100800

Cited by 62 publications

(24 citation statements)

References 31 publications

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“…Researchers in the field of meteorology have been interested in solar radiation for centuries. Irradiance forecasting has produced precise and accurate results in a number of recently conducted studies as a result of a variety of recently developed technologies [1]. PV is a technology that has been steadily increasing its share in the global power generation industry, which has made it a key player in the global energy market.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Dhanalakshmi¹,

Venkatesh²,

Ranjit

et al. 2022

International Journal of Photoenergy

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In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Dhanalakshmi¹,

Venkatesh²,

Ranjit

et al. 2022

International Journal of Photoenergy

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show abstract

“…By incorporating a CNN or GCN component into the RNN-based model, hybrid deep learning models such as CNN-LSTM [63], [64], GCN-LSTM [65], and GCN-GRU [66] were proposed for air pollution forecast, to take into account the spatial dependence of nearby observations including air pollution and auxiliary data such as meteorology and urban morphology. In addition to the RNN-based modelling, the one-dimensional CNN (1D-CNN) model was used to extract the temporal dependence of urban dynamics observed at a station [67] or nearby stations [68]. A self-attention variational autoencoder was developed to capture the time-series nature of air pollutants through latent space modelling [69].…”

Section: ) Air Pollution Forecastmentioning

confidence: 99%

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Zhang

Han

et al. 2022

IEEE Access

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Air pollution presents a serious health challenge in urban metropolises. While accurately monitoring and forecasting air pollution are highly crucial, existing data-driven models have yet fully captured the complex interactions between the temporal characteristics of air pollution and the spatial characteristics of urban dynamics. Our proposed Deep-AIR fills this gap to provide fine-grained citywide air pollution estimation and station-wide forecast, by exploiting domain-specific features (including Air Pollution, Weather, Urban Morphology, Transport, and Time-sensitive features), with a hybrid CNN-LSTM structure to capture the spatio-temporal features, and 1x1 convolution layers to enhance the learning of temporal and spatial interaction. Deep-AIR outperforms compatible baselines by a higher accuracy of 1.5%, 2.7%, and 2.3% for Hong Kong and 1.4%, 1.4% and 3.3% for Beijing in fine-grained 1-hr pollution estimation, and 1-hr and 24-hr forecasts, respectively. Saliency analysis reveals that for Hong Kong, spatial features, including street canyon and road density, are the best predictors for NO 2 , while temporal features, including historical air pollutants and weather, are the best predictors for PM 2.5 . For Beijing, historical air pollutant data, traffic congestion, wind direction and seasonal indicator are the best predictors for all pollutants. PM 10 in Hong Kong is achieving the best estimation and forecast accuracy, whilst CO in Beijing is achieving the best results.

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“…Bai et al proposed the diurnal-cycle-constrained empirical orthogonal function to reconstruct data from PM 2.5 sites across China (Bai et al 2019). Samal et al proposed the multi-directional time convolution artificial neural network to interpolate the PM 2.5 characteristic matrix (Samal et al 2021). Xu et al repaired the air pollution data of 61 monitoring sites in Guilin based on Gaussian diffusion and gate recurrent unit (Xu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing global PM_2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM

Tan

Wang

Yuan

et al. 2022

Environ. Res. Lett.

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Fine particulate matter (PM2.5) is widely concerned for its harmful impacts on global environment and human health, making air pollution monitoring so crucial and indispensable. As the world's first open, real-time, and historical air quality platform, OpenAQ collects and provides government measurement and research-level data from various channels. However, despite OpenAQ's innovation in providing us with ground-measured PM2.5 worldwide, we find significant data gaps in time series for most of the sites. The incompleteness of the data directly affects the public perception of PM2.5 concentration levels and hinders the progress of research related to air pollution. To address these issues, a two-step hybrid model named ST-SILM, i.e., Spatio-Temporal Model with Single Exponential Smoothing-Inverse Distance Weighted (SES-IDW) and Long Short-Term Memory (LSTM), is proposed to repair the missing data from PM2.5 sites worldwide collected from OpenAQ from 2017 to 2019. Both spatio-temporal correlation and neighborhood fields are considered and established in the model. To be specific, single exponential smoothing and inverse distance weighted were firstly used to repair missing values, and secondly, the LSTM network was employed to reconstruct the time series of continuous missing data. After the global ground-measured PM2.5 was reconstructed, the Light Gradient Boosting Machine (Light-GBM) model was applied to remote sensing estimation of the original ground-measured PM2.5 and of the reconstructed ground-measured PM2.5 to further verify the performance of ST-SILM. Experiment results show that the estimation accuracy of the reconstructed dataset is better (R2 from 2017 to 2019 increased by 0.02, 0.02, and 0.01 compared with the original dataset). Therefore, it’s concluded that the proposed model can effectively reconstruct data from PM2.5 sites worldwide.

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Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: A deep learning approach

Cited by 62 publications

References 31 publications

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Reconstructing global PM_2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM

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Multi-directional temporal convolutional artificial neural network for PM2.5 forecasting with missing values: A deep learning approach

Cited by 62 publications

References 31 publications

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities

Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM

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Reconstructing global PM_2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM