Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh
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S. Pal | J. Mallick | A. Islam | M. Fattah | A. Elbeltagi | Md Aminul Islam | Rabin Chakraborty | B. Ghose | H. R. Naqvi | Muhammad Bilal | Mohammed Al Awadh | Most. Kulsuma Akther Kakoli
[1] S. K. Singh,et al. A Deep Learning approach to estimate Air Pollutants concentration levels in Delhi's Aerosphere , 2022, 2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT).
[2] S. K. Singh,et al. Machine learning-based time series models for effective CO_2 emission prediction in India , 2022, Environmental Science and Pollution Research.
[3] S. Kouadri,et al. Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast) , 2021, Applied Water Science.
[4] S. Kouadri,et al. Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India , 2021, Environmental Science and Pollution Research.
[5] Md. Arfan Ali,et al. Air pollution scenario over Pakistan: characterization and ranking of extremely polluted cities using long-term concentrations of aerosols and trace gases , 2021 .
[6] Ping Zhang,et al. Estimating PM2.5 concentration using the machine learning GA-SVM method to improve the land use regression model in Shaanxi, China. , 2021, Ecotoxicology and environmental safety.
[7] Mohd Talib Latif,et al. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia , 2021, Applied Sciences.
[8] Javed Mallick,et al. Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh , 2021, Ecological Indicators.
[9] M. J. Hosen,et al. Exposure to air pollution and COVID‐19 severity: A review of current insights, management, and challenges , 2021, Integrated environmental assessment and management.
[10] N. Nahar,et al. The Severity of Environmental Pollution in the Developing Countries and Its Remedial Measures , 2021, Earth.
[11] Kuldip Singh Sangwan,et al. A systematic literature review on machine tool energy consumption , 2020 .
[12] I. Rousta,et al. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh , 2020, SN Applied Sciences.
[13] Shweta Mishra,et al. A machine learning-based model to estimate PM2.5 concentration levels in Delhi's atmosphere , 2020, Heliyon.
[14] A. Islam,et al. Potential of RT, bagging and RS ensemble learning algorithms for reference evapotranspiration prediction using climatic data-limited humid region in Bangladesh , 2020 .
[15] A. Naqvi,et al. Improved air quality and associated mortalities in India under COVID-19 lockdown , 2020, Environmental Pollution.
[16] M. Rahman,et al. How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh , 2020, Science of The Total Environment.
[17] Seung-Hyun Cho,et al. Assessment of PM2.5 population exposure of a community using sensor-based air monitoring instruments and similar time-activity groups , 2020 .
[18] Shihab Ahmad Shahriar,et al. Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh , 2020, Air Quality, Atmosphere & Health.
[19] Miao Liu,et al. Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas. , 2020, The Science of the total environment.
[20] A. K. Majumder,et al. PM2.5 concentration and meteorological characteristics in Dhaka, Bangladesh , 2020, Bangladesh Journal of Scientific and Industrial Research.
[21] D. Broday,et al. Estimation of high-resolution PM2.5 over Indo-Gangetic Plain by fusion of satellite data, meteorology, and land use variables. , 2020, Environmental science & technology.
[22] P. Hopke,et al. Assessing the PM2.5 impact of biomass combustion in megacity Dhaka, Bangladesh. , 2020, Environmental pollution.
[23] Danlu Chen,et al. Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism. , 2020, Environment international.
[24] A. Islam,et al. Co-distribution, possible origins, status and potential health risk of trace elements in surface water sources from six major river basins, Bangladesh. , 2020, Chemosphere.
[25] Chih-Da Wu,et al. A hybrid kriging/land-use regression model with Asian culture-specific sources to assess NO2 spatial-temporal variations. , 2019, Environmental pollution.
[26] Yuanwen Zeng,et al. PM2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan , 2019, International journal of environmental research and public health.
[27] Michael Heimbinder,et al. Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea. , 2019, Environment international.
[28] M. Hossain,et al. Study on Ambient Particulate Matter (PM2.5) with Different Mode of Transportation in Dhaka City, Bangladesh , 2019, American Journal of Pure and Applied Biosciences.
[29] Xiliang Ni,et al. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data , 2019, Atmosphere.
[30] Wenbin Sun,et al. Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing–Tianjin–Hebei, China , 2019, Atmospheric Environment.
[31] Xintong Li,et al. Predicting ground-level PM2.5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach. , 2019, Environmental pollution.
[32] Runkui Li,et al. Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model. , 2018, Environmental pollution.
[33] Richard O. Sinnott,et al. Prediction of Air Pollution through Machine Learning Approaches on the Cloud , 2018, 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT).
[34] M. Agrawal,et al. Assessment of local and distant sources of urban PM2.5 in middle Indo-Gangetic plain of India using statistical modeling , 2018, Atmospheric Research.
[35] L. Knibbs,et al. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. , 2018, The Science of the total environment.
[36] Xintao Lin,et al. Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China , 2018 .
[37] George Christakos,et al. Improved space-time mapping of PM2.5 distribution using a domain transformation method , 2018 .
[38] J. Daoud. Multicollinearity and Regression Analysis , 2017 .
[39] I. Saraswat,et al. Estimation of PM10 concentration from Landsat 8 OLI satellite imagery over Delhi, India , 2017 .
[40] Kasturi Devi Kanniah,et al. Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia , 2017 .
[41] Qiuhong Tang,et al. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model , 2017 .
[42] Shih-Chun Candice Lung,et al. Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability. , 2017, Environmental pollution.
[43] Dieu Tien Bui,et al. Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS , 2017 .
[44] Zhaohui Xue,et al. Spatiotemporal Pattern of PM2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression , 2017, Scientific Reports.
[45] A. Morsy,et al. Leaf Dust Accumulation and Air Pollution Tolerance Indices of Three Plant Species Exposed to Urban Particulate Matter Pollution from a Fertilizer Factory , 2016 .
[46] M. Rana,et al. Trends in atmospheric particulate matter in Dhaka, Bangladesh, and the vicinity , 2016, Environmental Science and Pollution Research.
[47] Mohd Talib Latif,et al. Seasonal variability of PM 2.5 composition and sources in the Klang Valley urban-industrial environment , 2016 .
[48] P. Gupta,et al. Estimation of particulate matter from satellite- and ground-based observations over Hyderabad, India , 2015 .
[49] Hong-Lei Yang,et al. Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data , 2015, PloS one.
[50] Xin Fang,et al. Spatial modeling of PM2.5 concentrations with a multifactoral radial basis function neural network , 2015, Environmental Science and Pollution Research.
[51] Stefan Sperlich,et al. Generalized Additive Models , 2014 .
[52] Davor Z Antanasijević,et al. PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization. , 2013, The Science of the total environment.
[53] Mohammad Arhami,et al. Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations , 2013, Environmental Science and Pollution Research.
[54] Michal Krzyzanowski,et al. Satellite-based estimates of ground-level fine particulate matter during extreme events: A case study of the Moscow fires in 2010 , 2011 .
[55] P. J. García Nieto,et al. Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain) , 2011, Math. Comput. Model..
[56] M. Brauer,et al. Creating National Air Pollution Models for Population Exposure Assessment in Canada , 2011, Environmental health perspectives.
[57] B. R. Gurjar,et al. Human health risks in megacities due to air pollution , 2010 .
[58] Daniel J. Jacob,et al. Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: implications for the sensitivity of PM2.5 to climate change. , 2010 .
[59] R. Hoff,et al. An Improved Method for Estimating Surface Fine Particle Concentrations Using Seasonally Adjusted Satellite Aerosol Optical Depth , 2010, Journal of the Air & Waste Management Association.
[60] P. Gupta,et al. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach , 2009 .
[61] A. Valavanidis,et al. Airborne Particulate Matter and Human Health: Toxicological Assessment and Importance of Size and Composition of Particles for Oxidative Damage and Carcinogenic Mechanisms , 2008, Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews.
[62] Yang Liu,et al. Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5. , 2008, Environmental science & technology.
[63] Philip K. Hopke,et al. Investigation of sources of atmospheric aerosol at urban and semi-urban areas in Bangladesh , 2004 .
[64] B. Simoneit,et al. Biomass burning as the main source of organic aerosol particulate matter in Malaysia during haze episodes. , 2004, Chemosphere.
[65] Robert P. W. Duin,et al. Bagging, Boosting and the Random Subspace Method for Linear Classifiers , 2002, Pattern Analysis & Applications.
[66] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[67] Muhammad Bilal,et al. A New Approach for Estimation of Fine Particulate Concentrations Using Satellite Aerosol Optical Depth and Binning of Meteorological Variables , 2017 .
[68] D. Ridley,et al. Source sector and region contributions to concentration and direct radiative forcing of black carbon in China , 2016 .
[69] B. Ratha,et al. Study of Random Tree and Random Forest Data Mining Algorithms for Microarray Data Analysis , 2016 .
[70] Ki-Hyun Kim,et al. A review on the human health impact of airborne particulate matter. , 2015, Environment international.
[71] Liangfu Chen,et al. Estimating Ground-Level PM2.5 Using Fine-Resolution Satellite Data in the Megacity of Beijing, China , 2015 .
[72] Sushilkumar Rameshpant Kalmegh,et al. Comparative Analysis of WEKA Data Mining Algorithm RandomForest, RandomTree and LADTree for Classification of Indigenous News Data , 2015 .
[73] William L. Crosson,et al. Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model , 2014 .