ArticleNeural computing & applications2021
Long-term time-series pollution forecast using statistical and deep learning methods.
Article in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- CO and NOEnvironmental science and pollution research international · 2026Article
- Enhanced short-term prediction of urban PMJournal of environmental health science & engineering · 2025Article
- Article
- Temporal trends and predictive modeling of air pollutants in Delhi: a comparative study of artificial intelligence models.Scientific reports · 2024Article
- Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis.The Lancet regional health. Southeast Asia · 2024Article
- Article
- Pollutants-mediated viral hepatitis in different types: assessment of different algorithms and time series models.Scientific reports · 2024Article
- Flexible Bayesian Product Mixture Models for Vector Autoregressions.Journal of machine learning research : JMLR · 2024Article
- Time series forecasting methods in emergency contexts.Scientific reports · 2023Article
- Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model.PLoS neglected tropical diseases · 2023Article
- Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models.Technological forecasting and social change · 2022Article
- Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020.BMC infectious diseases · 2022Article
- Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities.Sensors (Basel, Switzerland) · 2021Article
- PMEnvironmental challenges (Amsterdam, Netherlands) · 2021Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt-Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available.
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