{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:10:50Z","timestamp":1775913050034,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T00:00:00Z","timestamp":1630800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2017YFC1405600"],"award-info":[{"award-number":["2017YFC1405600"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["2019GHY112034"],"award-info":[{"award-number":["2019GHY112034"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons.<\/jats:p>","DOI":"10.3390\/rs13173530","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T13:18:26Z","timestamp":1630934306000},"page":"3530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3560-2960","authenticated-orcid":false,"given":"Pei","family":"Du","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Zhe","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Jingwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Marine Geophysical Prospecting Branch, Bureau of Geophysical Prospecting Incorporated, China National Petroleum Corporation, Tianjin 300457, China"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"Marine Geophysical Prospecting Branch, Bureau of Geophysical Prospecting Incorporated, China National Petroleum Corporation, Tianjin 300457, China"}]},{"given":"Jianchang","family":"Yang","sequence":"additional","affiliation":[{"name":"Offshore Oil Engineering Company Limited, Tianjin 300461, China"}]},{"given":"Chuanping","family":"Qu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Li","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Shanwei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34853","DOI":"10.1364\/OE.26.034853","article-title":"Novel Lidar algorithm for horizontal visibility measurement and sea fog monitoring","volume":"26","author":"Xian","year":"2018","journal-title":"Opt. Express"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gao, Y., and Jiang, G. (2018, January 10\u201311). Research on Influencing Factors and Countermeasures of Fog Navigation in Weihai Harbour. Proceedings of the 5th International Conference on Education, Management, Arts, Economics and Social Science, Sanya, China.","DOI":"10.2991\/icemaess-18.2018.214"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1002\/joc.6200","article-title":"World marine fog analysis based on 58-years of ship observations","volume":"40","author":"Dorman","year":"2019","journal-title":"Int. J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11802-008-0027-z","article-title":"An analysis and modeling study of a sea fog event over the Yellow and Bohai Seas","volume":"7","author":"Fu","year":"2008","journal-title":"J. Ocean Univ. China"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.jqsrt.2014.09.021","article-title":"A method of detecting sea fogs using CALIOP data and its application to improve MODIS-based sea fog detection","volume":"153","author":"Wu","year":"2014","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chunyang, Z., Jianhua, W., Shanwei, L., Hui, S., and Yanfang, X. (2019, January 24\u201326). Sea fog detection using U-net deep learning model based on MODIS data. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8920979"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s13143-018-0050-y","article-title":"A New Application of Unsupervised Learning to Nighttime Sea Fog Detection","volume":"54","author":"Shin","year":"2018","journal-title":"Asia Pac. J. Atmos. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, J.-H., Yoo, J.-M., and Choi, Y.-S. (2021). Advanced Dual-Satellite Method for Detection of Low Stratus and Fog near Japan at Dawn from FY-4A and Himawari-8. Remote Sens., 13.","DOI":"10.3390\/rs13051042"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, D., Park, M.-S., Park, Y.-J., and Kim, W. (2020). Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree. Remote Sens., 12.","DOI":"10.3390\/rs12010149"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"151","DOI":"10.2151\/jmsj.2016-009","article-title":"An Introduction to Himawari-8\/9\u2014Japan\u2019s New-Generation Geostationary Meteorological Satellites","volume":"94","author":"Bessho","year":"2016","journal-title":"J. Meteorol. Soc. Jpn. Ser. II"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ryu, H., and Hong, S. (2020). Sea Fog Detection Based on Normalized Difference Snow Index Using Advanced Himawari Imager Observations. Remote Sens., 12.","DOI":"10.3390\/rs12091521"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3940\/rina.ijme.2015.a4.337","article-title":"Risk Assessment of Arctic Navigation by using Improved Fuzzy-AHP Approach","volume":"157","author":"Sahin","year":"2015","journal-title":"Int. J. Marit. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.ress.2006.04.011","article-title":"Formal safety assessment based on relative risks model in ship navigation","volume":"92","author":"Hu","year":"2007","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.oceaneng.2010.10.012","article-title":"A decision-making system to maritime risk assessment","volume":"38","author":"Balmat","year":"2011","journal-title":"Ocean Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.tra.2020.10.017","article-title":"An integrated risk assessment model for safe Arctic navigation","volume":"142","author":"Zhang","year":"2020","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.ssci.2015.10.004","article-title":"Use of fuzzy rule-based evidential reasoning approach in the navigational risk assessment of inland waterway transportation systems","volume":"82","author":"Zhang","year":"2016","journal-title":"Saf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ssci.2016.03.019","article-title":"Expert elicitation and Bayesian Network modeling for shipping accidents: A literature review","volume":"87","author":"Zhang","year":"2016","journal-title":"Saf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s10479-018-2768-4","article-title":"Models and computational algorithms for maritime risk analysis: A review","volume":"271","author":"Lim","year":"2018","journal-title":"Ann. Oper. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.ress.2013.04.006","article-title":"Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River","volume":"118","author":"Zhang","year":"2013","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1080\/03088839.2020.1730010","article-title":"Risk analysis of maritime accidents along the main route of the Maritime Silk Road: A Bayesian network approach","volume":"47","author":"Jiang","year":"2020","journal-title":"Marit. Policy Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ssci.2013.08.002","article-title":"Safety assessment of shipping routes in the South China Sea based on the fuzzy analytic hierarchy process","volume":"62","author":"Wang","year":"2014","journal-title":"Saf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107403","DOI":"10.1016\/j.oceaneng.2020.107403","article-title":"Assessing and mapping maritime transportation risk based on spatial fuzzy multi-criteria decision making: A case study in the South China sea","volume":"208","author":"Zhou","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.oceaneng.2019.02.016","article-title":"Navigation risk assessment method based on flow conditions: A case study of the river reach between the Three Gorges Dam and the Gezhouba Dam","volume":"175","author":"Zhang","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105968","DOI":"10.1016\/j.margeo.2019.105968","article-title":"Regional-Scale distributions of pollen and spore assemblages in alluvium around the Bohai Sea: An essential step toward understanding marine palynological sources in China","volume":"415","author":"Yang","year":"2019","journal-title":"Mar. Geol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.oceaneng.2014.09.010","article-title":"Wave characteristics analysis in Bohai Sea based on ECMWF wind field","volume":"91","author":"Lv","year":"2014","journal-title":"Ocean Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.atmosres.2019.06.008","article-title":"Impact of water vapor transfer on a Circum-Bohai-Sea heavy fog: Observation and numerical simulation","volume":"229","author":"Tian","year":"2019","journal-title":"Atmos. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1175\/2009JTECHA1281.1","article-title":"Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms","volume":"26","author":"Winker","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.jmarsys.2005.09.016","article-title":"The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system","volume":"65","author":"Chassignet","year":"2007","journal-title":"J. Mar. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mayer, L., Jakobsson, M., Allen, G., Dorschel, B., Falconer, R., Ferrini, V., Lamarche, G., Snaith, H., and Weatherall, P. (2018). The Nippon Foundation\u2014GEBCO Seabed 2030 Project: The Quest to See the World\u2019s Oceans Completely Mapped by 2030. Geosciences, 8.","DOI":"10.3390\/geosciences8020063"},{"key":"ref_31","unstructured":"Maritime Safety Administration of the People\u2019s Republic of China (2018). Sailing Direction On Chinese Coast: North Area, China Communications Press Limited Liability Company."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1175\/2007JTECHA950.1","article-title":"Detection of Fog and Low Cloud Boundaries with Ground-Based Remote Sensing Systems","volume":"25","author":"Nowak","year":"2008","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"95","DOI":"10.2112\/SI90-012.1","article-title":"An Algorithm for Daytime Sea Fog Detection over the Greenland Sea Based on MODIS and CALIOP Data","volume":"90","author":"Xiao","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_34","unstructured":"Hao, Z., Pan, D., Gong, F., and Chen, J. (September, January 31). Sea fog characteristics based on MODIS data and streamer model. Proceedings of the Remote Sensing of Clouds and the Atmosphere XIV, Berlin, Germany."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s12524-018-0891-y","article-title":"Ship Classification in SAR Images Using a New Hybrid CNN\u2013MLP Classifier","volume":"47","author":"Sharifzadeh","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Costache, R., Pham, Q.B., Corodescu-Ro\u0219ca, E., C\u00eempianu, C., Hong, H., Linh, N.T.T., Fai, C.M., Ahmed, A.N., Vojtek, M., and Pandhiani, S.M. (2020). Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use\/Land-Cover Changes and Flash-Flood Potential. Remote Sens., 12.","DOI":"10.3390\/rs12091422"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification","volume":"140","author":"Zhang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","first-page":"285","article-title":"Overview of Definitions of Maritime Safety, Safety at Sea, Navigational Safety and Safety in General","volume":"13","author":"Formela","year":"2019","journal-title":"TransNav Int. J. Mar. Navig. Saf. Sea Transp."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s42241-018-0050-5","article-title":"Case study on wave-current interaction and its effects on ship navigation","volume":"30","author":"Chen","year":"2018","journal-title":"J. Hydrodyn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1002\/met.1344","article-title":"Algorithm for sea fog monitoring with the use of information technologies","volume":"21","author":"Heo","year":"2014","journal-title":"J. Meteorol. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"012088","DOI":"10.1088\/1757-899X\/227\/1\/012088","article-title":"Numerical investigation of shallow water effect on a barge ship resistance","volume":"227","author":"Pacuraru","year":"2017","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/0305-0548(94)00059-H","article-title":"Determining objective weights in multiple criteria problems: The critic method","volume":"22","author":"Diakoulaki","year":"1995","journal-title":"Comput. Oper. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"149","DOI":"10.31181\/dmame2003149z","article-title":"Objective methods for determining criteria weight coefficients: A modification of the CRITIC method","volume":"3","year":"2020","journal-title":"Decis. Mak. Appl. Manag. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"76","DOI":"10.31181\/dmame210402076i","article-title":"Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD","volume":"4","author":"Mukhametzyanov","year":"2021","journal-title":"Decis. Mak. Appl. Manag. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1017\/S0266466600005831","article-title":"Estimating Multiple Breaks One at a Time","volume":"13","author":"Bai","year":"1997","journal-title":"Econ. Theory"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apgeog.2013.08.005","article-title":"Spatially-Explicit sensitivity analysis for land suitability evaluation","volume":"45","author":"Xu","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"104858","DOI":"10.1016\/j.ssci.2020.104858","article-title":"A Dynamic Bayesian Network model for ship-ice collision risk in the Arctic waters","volume":"130","author":"Khan","year":"2020","journal-title":"Saf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3530\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:56:56Z","timestamp":1760165816000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,5]]},"references-count":47,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173530"],"URL":"https:\/\/doi.org\/10.3390\/rs13173530","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,5]]}}}