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Machine learning al...
Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters
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- Tao, Hai (författare)
- School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China; State Key Laboratory of Public Big Data, Guizhou University, Guizhou, Guiyang 550025, China; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
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- Jawad, Ali H. (författare)
- Faculty of Applied Sciences, UniversitiTeknologi MARA, 40450 Shah Alam, Selangor, Malaysia
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- Shather, A.H. (författare)
- Dep of Computer Technology Engineering, Engineering Technical College, University of Alkitab, Iraq
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- Al-Khafaji, Zainab (författare)
- Department of Building and Construction Technologies Engineering, AL-Mustaqbal University College, Hillah 51001, Iraq
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- Rashid, Tarik A. (författare)
- Department of Building and Construction Technologies Engineering, AL-Mustaqbal University College, Hillah 51001, Iraq
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- Ali, Mumtaz (författare)
- UniSQ College, University of Southern Queensland, QLD 4350, Australia
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- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
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- Marhoon, Haydar Abdulameer (författare)
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq; College of Computer Sciences and Information Technology, University of Kerbala, Karbala, Iraq
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- Shahid, Shamsuddin (författare)
- Department of Hydraulics and Hydrology, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudia, Johor, Malaysia
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- Yaseen, Zaher Mundher (författare)
- Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
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(creator_code:org_t)
- Elsevier, 2023
- 2023
- Engelska.
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Ingår i: Environment International. - : Elsevier. - 0160-4120 .- 1873-6750. ; 175
- Relaterad länk:
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Geotechnical Engineering (hsv//eng)
Nyckelord
- Air quality prediction
- PM2.5 concentration
- Simulated annealing
- Machine learning
- Arid climate
- Geoteknik
- Soil Mechanics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Tao, Hai
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Jawad, Ali H.
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Shather, A.H.
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Al-Khafaji, Zain ...
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Rashid, Tarik A.
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Ali, Mumtaz
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visa fler...
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Al-Ansari, Nadhi ...
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Marhoon, Haydar ...
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Shahid, Shamsudd ...
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Yaseen, Zaher Mu ...
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