Time Series Analysis to Forecast Air Quality Indices in Thiruvananthapuram District, Kerala, India

Naveen, _______; Anu, N.

doi:10.9790/9622-0706036684

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…For instance, Xiang-Li et al (2017) analysed air quality in Beijing from 2014 to 2016 using a novel long shortterm memory neural network extended (LSTME) model, and showed that this specification is superior to others to model time series with long-term dependence and to capture spatio-temporal correlations and improve predictions. Naveen et al (2017) estimated ARIMA and SARIMA models to study air quality in India, and found that the former outperforms the latter. Zhongfei et al (2016) analysed pollution in four Chinese cities from 2013 to 2015 using fractional integration methods and found a high degree of persistence.…”

Section: Literature Reviewmentioning

confidence: 99%

Atmospheric Pollution in Chinese Cities: Trends and Persistence

2022

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Section: Literature Reviewmentioning

confidence: 99%

Atmospheric Pollution in Chinese Cities: Trends and Persistence

2022

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“…Accurate air quality predictions are useful, for example, individual activity arrangements and government pollution restrictions can benefit from air quality time series in New Delhi. Naveen et al [13] then adopted the seasonal autoregressive integrated moving average (SARIMA), which can capture the seasonal feature of time series, to predict the air quality in Kerala. However, due to the complexity and uncertainty of air quality prediction tasks, it is difficult for the statistical methods to perform well for longterm predictions.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions

Xu¹,

Wang²,

Ying³

et al. 2023

Preprint

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Air quality prediction is a typical Spatio-temporal modeling problem, which always uses different components to handle spatial and temporal dependencies in complex systems separately. Previous models based on time series analysis and Recurrent Neural Network (RNN) methods have only modeled time series while ignoring spatial information. Previous GCNs-based methods usually require providing spatial correlation graph structure of observation sites in advance. The correlations among these sites and their strengths are usually calculated using prior information. However, due to the limitations of human cognition, limited prior information cannot reflect the real station-related structure or bring more effective information for accurate prediction. To this end, we propose a novel Dynamic Graph Neural Network with Adaptive Edge Attributes (DGN-AEA) on the message passing network, which generates the adaptive bidirected dynamic graph by learning the edge attributes as model parameters. Unlike prior information to establish edges, our method can obtain adaptive edge information through end-to-end training without any prior information. Thus reduced the complexity of the problem. Besides, the hidden structural information between the stations can be obtained as model by-products, which can help make some subsequent decision-making analyses. Experimental results show that our model received state-of-the-art performance than other baselines.

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“…In India, Naveen and Anu [22] analyzed and forecasted the varying trend of outdoor air quality using the dataset recorded at different monitoring air quality stations in the district of Thiruvananthapuram, Kerala, India. The analysis was conducted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and ARIMA models, and ARIMA model was observed to have performed better in giving more accurate forecast values.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Degree of Persistence, Trend and the Best Time Series Forecasting Models for Particulate Matter (PM10) Pollutant Across Malaysia

Isma'il,

Awang,

Kane

2023

Mal. J. Fund. Appl. Sci.

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Particulate matter is the most common atmospheric pollutant with some negative consequences on human health, environment, and the ambient air quality. In this study, the concentration of particulate matter in sixty-five air quality monitoring stations across Malaysia during January to December 2018 is analyzed. We investigated the degree of persistence and trend of the particulate matter series and developed a forecasting model using both the autoregressive integrated moving average (ARIMA) and the autoregressive fractionally integrated moving average (ARFIMA) time series methods for each monitoring station separately. Mean absolute deviation (MAD), mean absolute percentage error (MAPE) and root mean square error (RMSE) are used to determine the best fitted model for forecasting each monitoring station. Ljung-Box test of uncorrelated residuals confirmed the adequacy of each of the model. The results confirmed the evidence of transitory form of persistence in the level of particulate matter pollutant at sixty-four monitoring stations while trend increases in seventeen monitoring stations. Forecast error analysis indicates that ARFIMA models performed better than ARIMA models by producing smaller RMSE values in forty-two of the sixty-five monitoring stations. However, the overall result indicates that none of the model could be regarded as universal in forecasting particulate matter concentration, and their performance is independent of the category or location of a given monitoring station.

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Time Series Analysis to Forecast Air Quality Indices in Thiruvananthapuram District, Kerala, India

Cited by 11 publications

References 15 publications

Atmospheric Pollution in Chinese Cities: Trends and Persistence

Atmospheric Pollution in Chinese Cities: Trends and Persistence

Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions

Investigating the Degree of Persistence, Trend and the Best Time Series Forecasting Models for Particulate Matter (PM10) Pollutant Across Malaysia

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