{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:05:44Z","timestamp":1771956344012,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41875041"],"award-info":[{"award-number":["41875041"]}]},{"name":"National Natural Science Foundation of China","award":["2008085j19"],"award-info":[{"award-number":["2008085j19"]}]},{"name":"National Natural Science Foundation of China","award":["AHL 2021QN01"],"award-info":[{"award-number":["AHL 2021QN01"]}]},{"name":"Anhui Provincial Natural Science Foundation","award":["41875041"],"award-info":[{"award-number":["41875041"]}]},{"name":"Anhui Provincial Natural Science Foundation","award":["2008085j19"],"award-info":[{"award-number":["2008085j19"]}]},{"name":"Anhui Provincial Natural Science Foundation","award":["AHL 2021QN01"],"award-info":[{"award-number":["AHL 2021QN01"]}]},{"name":"Youth Fund Project of the Advanced Laser Technology Laboratory of Anhui Province","award":["41875041"],"award-info":[{"award-number":["41875041"]}]},{"name":"Youth Fund Project of the Advanced Laser Technology Laboratory of Anhui Province","award":["2008085j19"],"award-info":[{"award-number":["2008085j19"]}]},{"name":"Youth Fund Project of the Advanced Laser Technology Laboratory of Anhui Province","award":["AHL 2021QN01"],"award-info":[{"award-number":["AHL 2021QN01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To improve the accuracy of atmospheric visibility (V) prediction based on machine learning in different pollution scenarios, a new atmospheric visibility prediction method based on the stacking fusion model (VSFM) is established in this paper. The new method uses the stacking strategy to fuse two base learners\u2014eXtreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM)\u2014to optimize prediction accuracy. Furthermore, seasonal feature importance evaluations and feature selection were utilized to optimize prediction accuracy in different seasons with different pollution sources. The new VSFM was applied to 1-year environmental and meteorological data measured in Qingdao, China. Compared to other traditional non-stacking models, the new VSFM improved precision during different seasons, especially in extremely low-visibility scenarios (V&lt; 2 km). The TS score of the VSFM was significantly better than that of other models. For extremely low-visibility scenarios, the VSFM had a threat score (TS) of 0.5, while the best performance of other models was less than 0.27. The new method is promising for atmospheric visibility prediction under complex urban pollution conditions. The research results can also improve our understanding of the factors that influence urban visibility.<\/jats:p>","DOI":"10.3390\/rs15051450","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Application of a Fusion Model Based on Machine Learning in Visibility Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Maochan","family":"Zhen","sequence":"first","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Mingjian","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230009, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9959-2453","authenticated-orcid":false,"given":"Tao","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Feifei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Kaixuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Xuebin","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7949-6170","authenticated-orcid":false,"given":"Shengcheng","family":"Cui","sequence":"additional","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]},{"given":"Xuebin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China"},{"name":"Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China"},{"name":"Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1016\/0004-6981(81)90214-6","article-title":"Atmospheric visibility","volume":"15","author":"Horvath","year":"1981","journal-title":"Atmos. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1016\/j.atmosenv.2007.11.025","article-title":"Long-term trend of visibility and its characterizations in the Pearl River Delta (PRD) region, China","volume":"42","author":"Deng","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1007\/s00376-019-8252-5","article-title":"Applying anomaly-based weather analysis to the prediction of low visibility associated with the coastal fog at Ningbo-Zhoushan Port in East China","volume":"36","author":"Qian","year":"2019","journal-title":"Adv. Atmos. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1007\/s00024-019-02168-6","article-title":"A review of high impact weather for aviation meteorology","volume":"176","author":"Gultepe","year":"2019","journal-title":"Pure Appl. Geo-Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.envint.2012.11.008","article-title":"Communicating air pollution-related health risks to the public: An application of the Air Quality Health Index in Shanghai, China","volume":"51","author":"Chen","year":"2013","journal-title":"Environ. Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4646","DOI":"10.1016\/j.atmosenv.2006.04.018","article-title":"Comment on \u201cfully coupled \u2018online\u2019 chemistry within the WRF model\u201d, by Grell et al., 2005. Atmospheric Environment 39, 6957\u20136975","volume":"40","author":"Jacobson","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4183","DOI":"10.1029\/2001JD001409","article-title":"Models-3 community multiscale air quality (cmaq) model aerosol component 1. model description","volume":"108","author":"Binkowski","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"117909","DOI":"10.1016\/j.atmosenv.2020.117909","article-title":"Evaluation of real-time PM 2.5 forecasts with the WRF-CMAQ modeling system and weather-pattern-dependent bias-adjusted PM 2.5 forecasts in Taiwan","volume":"244","author":"Cheng","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6957","DOI":"10.1016\/j.atmosenv.2005.04.027","article-title":"Fully coupled \"online\" chemistry within the WRF model","volume":"39","author":"Grell","year":"2005","journal-title":"Atmos. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.atmosenv.2017.01.020","article-title":"Numerical air quality forecasting over eastern China: An operational application of WRF-Chem\u2014ScienceDirect","volume":"153","author":"Zhou","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.5194\/gmd-9-2153-2016","article-title":"Development of an adjoint model of GRAPES\u2013CUACE and its application in tracking influential haze source areas in north China","volume":"9","author":"An","year":"2016","journal-title":"Geosci. Model Dev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1175\/2009WAF2222337.1","article-title":"High-Resolution GEM-LAM Application in Marine Fog Prediction: Evaluation and Diagnosis","volume":"25","author":"Yang","year":"2009","journal-title":"Weather Forecast."},{"key":"ref_13","first-page":"1","article-title":"Analysis of Factors Affecting Visibility and Its Variation Features in Pudong Area of Shanghai","volume":"02","author":"Shi","year":"2008","journal-title":"Atmos. Sci. Res. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"20150257","DOI":"10.1098\/rspa.2015.0257","article-title":"Assessing Beijing\u2019s PM2.5 pollution: Severity, weather impact, APEC and winter heating","volume":"471","author":"Liang","year":"2015","journal-title":"Proc. R. Soc. A"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, H., Wei, Z., and Liu, J. (2022, January 22\u201425). Visibility Prediction Based On XGBoost And Markov Chain Combined Model. Proceedings of the 7th International Conference on Computer and Communications (ICCC), Wuhan, China.","DOI":"10.1109\/ICCC54389.2021.9674371"},{"key":"ref_16","first-page":"1035","article-title":"Visibility forecast model based on LightGBM algorithm","volume":"41","author":"Yu","year":"2021","journal-title":"J. Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kim, B.Y., Cha, J.W., Chang, K.H., and Lee, C. (2021). Visibility Prediction over South Korea Based on Random Forest. Atmos., 12.","DOI":"10.3390\/atmos12050552"},{"key":"ref_18","first-page":"40","article-title":"Meteorology visibility estimation by using multi-support vector regression method","volume":"11","author":"Lo","year":"2020","journal-title":"J. Adv. Inf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s00521-010-0363-y","article-title":"Support vector regression with reduced training sets for air temperature prediction: A comparison with artificial neural networks","volume":"20","author":"Chevalier","year":"2011","journal-title":"Neural Comput. Appl."},{"key":"ref_20","unstructured":"Bari, D. (November, January 29). Visibility Prediction Based on Kilometric NWP Model Outputs Using Machine-Learning Regression. Proceedings of the 14th International Conference on e-Science (e-Science), Amsterdam, Netherlands."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.atmosres.2018.07.017","article-title":"Prediction of low-visibility events due to fog using ordinal classification","volume":"214","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1007\/s00024-018-1863-4","article-title":"Probabilistic nowcasting of low-visibility procedure states at Vienna International Airport during cold season","volume":"176","author":"Kneringer","year":"2019","journal-title":"Pure Appl. Geo-Phys."},{"key":"ref_23","first-page":"314","article-title":"The application of deep learning in airport visibility forecast","volume":"7","author":"Zhu","year":"2017","journal-title":"Atmos. Clim. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jclepro.2016.05.067","article-title":"Impacts of meteorological condition and aerosol chemical compositions on visibility impairment in Nanjing, China","volume":"131","author":"Yu","year":"2016","journal-title":"J. Clean Prod."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13260","DOI":"10.1021\/acs.est.8b02917","article-title":"An ensemble machine-learning model to predict historical PM2. 5 concentrations in China from satellite data","volume":"52","author":"Xiao","year":"2018","journal-title":"Environ. Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1016\/j.envpol.2018.08.029","article-title":"Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2. 5","volume":"242","author":"Xu","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"74776","DOI":"10.1109\/ACCESS.2019.2920865","article-title":"Weather Visibility Prediction Based on Multimodal Fusion","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_29","unstructured":"Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., and Chen, K. (2015). Xgboost: Extreme Gradient Boosting, R Core Team. Volume 1."},{"key":"ref_30","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017, January 8\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"128","DOI":"10.4209\/aaqr.2019.08.0408","article-title":"Application of the XGBoost Machine Learning Method in PM2.5 Prediction: A Case Study of Shanghai","volume":"20","author":"Ma","year":"2020","journal-title":"Aerosol Air Qual. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.scitotenv.2018.04.040","article-title":"Development of a stacked ensemble model for forecasting and analyzing daily average PM 2.5 concentrations in Beijing, China","volume":"635","author":"Zhai","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"nwaa307","DOI":"10.1093\/nsr\/nwaa307","article-title":"Robust prediction of hourly PM2.5 from meteorological data using Light GBM","volume":"8","author":"Zhong","year":"2021","journal-title":"Natl. Sci. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","article-title":"Essemble based systems in decision making","volume":"6","author":"Polikar","year":"2006","journal-title":"IEEE Circ. Syst. Mag."},{"key":"ref_35","first-page":"1","article-title":"Analysis of the influence of changes in marine meteorological conditions on the advection fog process in Qingdao","volume":"40","author":"Sheng","year":"2010","journal-title":"Period. Ocean Univ. China"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1450\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:47:58Z","timestamp":1760122078000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":35,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051450"],"URL":"https:\/\/doi.org\/10.3390\/rs15051450","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,4]]}}}