{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:45:40Z","timestamp":1787118340000,"version":"3.56.0"},"reference-count":73,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T00:00:00Z","timestamp":1651881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Recent years include the world\u2019s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017, focusing on ML and DL developed methods for disaster management. In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage assessment and post-disaster response as well as cases studies. Furthermore, some recently developed ML and DL applications for disaster management have been analyzed. A discussion of the findings is provided as well as directions for further research.<\/jats:p>","DOI":"10.3390\/make4020020","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T21:31:20Z","timestamp":1652045480000},"page":"446-473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":285,"title":["Machine Learning in Disaster Management: Recent Developments in Methods and Applications"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0841-2926","authenticated-orcid":false,"given":"Vasileios","family":"Linardos","sequence":"first","affiliation":[{"name":"Archeiothiki S.A., GR-19400 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9322-7324","authenticated-orcid":false,"given":"Maria","family":"Drakaki","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, University Center of International Programmes of Studies, International Hellenic University, 14th Km Thessaloniki-N. Moudania, GR-57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Tzionas","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, International Hellenic University, P.O. Box 141, GR-57400 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7390-3249","authenticated-orcid":false,"given":"Yannis","family":"Karnavas","sequence":"additional","affiliation":[{"name":"Electrical Machines Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, GR-67100 Xanthi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,7]]},"reference":[{"key":"ref_1","unstructured":"Centre for Research on the Epidemiology of Disasters (CRED), and United Nations Office for Disaster Risk Reduction (UNDRR) (2021, October 04). Global trends and Perspectives Executive Summary. Available online: https:\/\/www.undrr.org\/publication\/2020-non-COVID-year-disasters."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.ejor.2005.05.016","article-title":"OR\/MS research in disaster operations management","volume":"175","author":"Altay","year":"2006","journal-title":"Eur. J. Oper. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1007\/s11069-020-04124-3","article-title":"Applications of Artificial Intelligence for Disaster Management","volume":"103","author":"Sun","year":"2020","journal-title":"Nat. Haz."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102282","DOI":"10.1016\/j.ijdrr.2021.102282","article-title":"Investigating the impact of site management on distress in refugee sites using Fuzzy Cognitive Maps","volume":"60","author":"Drakaki","year":"2021","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.ijdrr.2018.06.013","article-title":"An intelligent multi-agent based decision support system for refugee settlement siting","volume":"31","author":"Drakaki","year":"2018","journal-title":"Int. J. Disaster. Risk Reduct."},{"key":"ref_6","unstructured":"United Nations Office for Disaster Risk Reduction (UNDRR) (2009). UNISDR Terminology on Disaster Risk Reduction, UNISDR. Available online: https:\/\/www.unisdr.org\/files\/7817_UNISDRTerminologyEnglish.pdf."},{"key":"ref_7","unstructured":"(2021, October 04). EM-DAT\u2014The International Disasters Database. Available online: https:\/\/www.emdat.be\/guidelines."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1057\/palgrave.jors.2602125","article-title":"Blackett memorial lecture humanitarian aid logistics: Supply chain management in high gear","volume":"57","year":"2006","journal-title":"J. Oper. Res. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Arinta, R.R., and Andi, E.W.R. (2019, January 20\u201321). Natural disaster application on big data and machine learning: A review. Proceedings of the 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia.","DOI":"10.1109\/ICITISEE48480.2019.9003984"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yu, M., Yang, C., and Li, Y. (2018). Big data in natural disaster management: A review. Geosciences, 8.","DOI":"10.3390\/geosciences8050165"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Presa-Reyes, M., and Chen, S.C. (2020, January 6\u20138). Assessing Building Damage by Learning the Deep Feature Correspondence of before and after Aerial Images. Proceedings of the 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), Shenzhen, China.","DOI":"10.1109\/MIPR49039.2020.00017"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Akshya, J., and Priyadarsini, P.L.K. (2019, January 21\u201323). A hybrid machine learning approach for classifying aerial images of flood-hit areas. Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India.","DOI":"10.1109\/ICCIDS.2019.8862138"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10478","DOI":"10.1109\/ACCESS.2020.2965550","article-title":"A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations from Social Media in Disasters","volume":"8","author":"Fan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","unstructured":"Ben-Hur, A., Horn, D., Siegelmann, H.T., and Vapnik, V. (2000, January 3\u20137). A support vector clustering method. Proceedings of the 15th International Conference on Pattern Recognition. ICPR-2000, Barcelona, Spain."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1214\/009053607000000677","article-title":"Kernel methods in machine learning","volume":"36","author":"Hofmann","year":"2008","journal-title":"Ann. Statist."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"O\u2019Connor, J., Eberle, C., Cotti, D., Hagenlocher, M., Hassel, J., Janzen, S., Narvaez, L., Newsom, A., Ortiz-Vargas, A., and Schuetze, S. (2021). Interconnected Disaster Risks. UNU-EHS, 64. Available online: https:\/\/reliefweb.int\/report\/world\/interconnected-disaster-risks-20202021.","DOI":"10.53324\/NYHZ4182"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"68917","DOI":"10.1109\/ACCESS.2021.3074819","article-title":"Automated Machine Learning Approaches for Emergency Response and Coordination via Social Media in the Aftermath of a Disaster: A Review","volume":"9","author":"Dwarakanath","year":"2021","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1007\/s00366-019-00798-x","article-title":"Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence","volume":"36","author":"Yuan","year":"2020","journal-title":"Eng. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.2166\/wcc.2019.321","article-title":"Flood prediction based on weather parameters using deep learning","volume":"11","author":"Sankaranarayanan","year":"2019","journal-title":"J. Water Clim. Chang."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s11069-017-3122-x","article-title":"Fuzzy neural network and LLE algorithm for forecasting precipitation in tropical cyclones: Comparisons with interpolation method by ECMWF and stepwise regression method","volume":"91","author":"Huang","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s11069-016-2579-3","article-title":"Earthquake magnitude prediction in Hindukush region using machine learning techniques","volume":"85","author":"Asim","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.cogsys.2020.11.002","article-title":"Earthquake disaster avoidance learning system using deep learning","volume":"66","author":"Amin","year":"2021","journal-title":"Cogn. Syst. Res."},{"key":"ref_25","first-page":"1892209","article-title":"Novel ensemble machine learning models in flood susceptibility mapping","volume":"26","author":"Prasad","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1080\/19475705.2020.1785555","article-title":"Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment","volume":"11","author":"Nsengiyumva","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s12665-016-6374-y","article-title":"Shallow landslide susceptibility assessment using a novel hybrid intelligence approach","volume":"76","author":"Shirzadi","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35344","DOI":"10.1109\/ACCESS.2019.2904457","article-title":"Multi-Network Vulnerability Causal Model for Infrastructure Co-Resilience","volume":"7","author":"Sriram","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"012056","DOI":"10.1088\/1755-1315\/169\/1\/012056","article-title":"Flood vulnerability assessment using artificial neural networks in Muar Region, Johor Malaysia","volume":"169","author":"Wahab","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mutlu, B., Nefeslioglu, H.A., Sezer, E.A., Ali, A.M., and Gokceoglu, C. (2019). An experimental research on the use of recurrent neural networks in landslide susceptibility mapping. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120578"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"Catena"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gupta, T., and Roy, S. (October, January 26). A Hybrid Model based on Fused Features for Detection of Natural Disasters from Satellite Images. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324611"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Layek, A.K., Poddar, S., and Mandal, S. (2019, January 25\u201328). Detection of flood images posted on online social media for disaster response. Proceedings of the 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), Gangtok, India.","DOI":"10.1109\/ICACCP.2019.8882877"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neucom.2017.04.083","article-title":"Early fire detection using convolutional neural networks during surveillance for effective disaster management","volume":"288","author":"Muhammad","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_35","unstructured":"Lee, W., Kim, S., Lee, Y.-T., Lee, H.-W., and Choi, M. (2017, January 8\u201310). Deep neural networks for wild fire detection with unmanned aerial vehicle. Proceedings of the 2017 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8867","DOI":"10.1109\/TGRS.2019.2923453","article-title":"Learn to Detect: Improving the Accuracy of Earthquake Detection","volume":"57","author":"Chin","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4773","DOI":"10.1029\/2018GL077870","article-title":"Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning","volume":"45","author":"Li","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1016\/j.jhydrol.2018.11.060","article-title":"Application of machine learning to an early warning system for very short-term heavy rainfall","volume":"568","author":"Moon","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gopal, L.S., Prabha, R., Pullarkatt, D., and Ramesh, M.V. (November, January 29). Machine Learning based Classification of Online News Data for Disaster Management. Proceedings of the 2020 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA.","DOI":"10.1109\/GHTC46280.2020.9342921"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Domala, J., Dogra, M., Masrani, V., Fernandes, D., D\u2019Souza, K., Fernandes, D., and Carvalho, T. (2020, January 2\u20134). Automated Identification of Disaster News for Crisis Management using Machine Learning and Natural Language Processing. Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India.","DOI":"10.1109\/ICESC48915.2020.9156031"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, T., Tao, Y., Chen, S.C., and Shyu, M.L. (2020, January 6\u20138). Multi-Task Multimodal Learning for Disaster Situation Assessment. Proceedings of the 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), Shenzhen, China.","DOI":"10.1109\/MIPR49039.2020.00050"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1080\/15230406.2017.1356242","article-title":"Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment","volume":"45","author":"Resch","year":"2018","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1061\/(ASCE)1527-6988(2006)7:2(72)","article-title":"HAZUS-MH Flood Loss Estimation Methodology. II. Damage and Loss Assessment","volume":"7","author":"Scawthorn","year":"2006","journal-title":"Nat. Hazards Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13393","DOI":"10.1007\/s00500-019-03878-8","article-title":"Analysis of remote sensing imagery for disaster assessment using deep learning: A case study of flooding event","volume":"23","author":"Yang","year":"2019","journal-title":"Soft Comput."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Ofli, F., Imran, M., and Mitra, P. (August, January 31). Damage assessment from social media imagery data during disasters. Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia.","DOI":"10.1145\/3110025.3110109"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rizk, Y., Jomaa, H., Awad, M., and Castillo, C. (2019, January 8\u201312). A computationally efficient multi-modal classification approach of disaster-related Twitter images. Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing (SAC \u201919), Limassol, Cyprus.","DOI":"10.1145\/3297280.3297481"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dotel, S., Shrestha, A., Bhusal, A., Pathak, R., Shakya, A., and Panday, S. (2020, January 20\u201322). Disaster Assessment from Satellite Imagery by Analysing Topographical Features Using Deep Learning. Proceedings of the IVSP \u201920: 2020 2nd International Conference on Image, Video and Signal Processing, Singapore.","DOI":"10.1145\/3388818.3389160"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, X., Zhang, H., Caragea, D., and Imran, M. (2018, January 28\u201331). Localizing and quantifying damage in social media images. Proceedings of the 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Spain.","DOI":"10.1109\/ASONAM.2018.8508298"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"101682","DOI":"10.1016\/j.ijdrr.2020.101682","article-title":"A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster","volume":"49","author":"Lin","year":"2020","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"O\u2019Neal, A., Rodgers, B., Segler, J., Murthy, D., Lakuduva, N., Johnson, M., and Stephens, K. (2018, January 17\u201320). Training an Emergency-Response Image Classifier on Signal Data. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00119"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101325","DOI":"10.1016\/j.ijdrr.2019.101325","article-title":"Analysis of medical rescue strategies based on a rough set and genetic algorithm: A disaster classification perspective","volume":"42","author":"Li","year":"2019","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ehara, K., Aljehani, M., Yokemura, T., and Inoue, M. (2020, January 4\u20136). Individual status recognition system assisted by UAV in post-disaster. Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE46568.2020.9043101"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.trd.2019.03.002","article-title":"Harnessing the power of machine learning: Can Twitter data be useful in guiding resource allocation decisions during a natural disaster?","volume":"77","author":"Reynard","year":"2019","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113234","DOI":"10.1016\/j.dss.2019.113234","article-title":"Exploring the role of deep neural networks for post-disaster decision support","volume":"130","author":"Chaudhuri","year":"2020","journal-title":"Decis. Support Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1111\/1468-5973.12194","article-title":"Disaster response aided by tweet classification with a domain adaptation approach","volume":"26","author":"Li","year":"2018","journal-title":"J. Contingencies Crisis Manag."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Bejiga, M.B., Zeggada, A., Nouffidj, A., and Melgani, F. (2017). A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery. Remote Sens., 9.","DOI":"10.3390\/rs9020100"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"100030","DOI":"10.1016\/j.pdisas.2019.100030","article-title":"Using a combination of human insights and \u2018deep learning\u2019 for real-time disaster communication","volume":"2","author":"Robertson","year":"2019","journal-title":"Prog. Disaster Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1080\/17538947.2019.1633425","article-title":"Identifying disaster related social media for rapid response: A visual-textual fused CNN architecture","volume":"13","author":"Huang","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kundu, S., Srijith, P.K., and Desarkar, M.S. (2018, January 28\u201331). Classification of short-texts generated during disasters: A deep neural network based approach. Proceedings of the 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain.","DOI":"10.1109\/ASONAM.2018.8508695"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/TCSS.2019.2914179","article-title":"Extracting Resource Needs and Availabilities from Microblogs for Aiding Post-Disaster Relief Operations","volume":"6","author":"Basu","year":"2019","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_61","unstructured":"Neppalli, V.K., Caragea, C., and Caragea, D. (2018, January 20\u201323). Deep Neural Networks versus Na\u00efve Bayes Classifiers for Identifying Informative Tweets during Disasters. Proceedings of the 15th ISCRAM Conference, Rochester, NY, USA."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Paul, U., Ermakov, A., Nekrasov, M., Adarsh, V., and Belding, E. (2020, January 20\u201324). #Outage: Detecting Power and Communication Outages from Social Networks. Proceedings of the Web Conference 2020, Taipei, Taiwan.","DOI":"10.1145\/3366423.3380251"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kabir, M.d.Y., and Madria, S. (2019, January 2\u20135). A Deep Learning Aproach for Tweet Classification and Rescue Scheduling for Effective Disaster Management. Proceedings of the 27th ACM Sigspatial International Conference on Advances in Geographic Information Systems, Chicago, IL, USA.","DOI":"10.1145\/3347146.3359097"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Peng, B., Liu, X., Meng, Z., and Huang, Q. (2019, January 5). Urban Flood Mapping with Residual Patch Similarity Learning. Proceedings of the 3rd ACM Sigspatial International Workshop on AI for Geographic Knowledge Discovery, Chicago, IL, USA.","DOI":"10.1145\/3356471.3365235"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Nagendra, N.P., Narayanamurthy, G., and Moser, R. (2020). Management of humanitarian relief operations using satellite big data analytics: The case of Kerala floods. Ann. Oper. Res., 1\u201326.","DOI":"10.1007\/s10479-020-03593-w"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Laverdiere, M., Yang, L., Tuttle, M., and Vaughan, C. (October, January 26). Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324160"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1080\/17538947.2018.1563219","article-title":"Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: A case study of Hurricane Irma","volume":"12","author":"Sit","year":"2019","journal-title":"Int. J. Digit. Earth."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cageo.2017.11.019","article-title":"Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China","volume":"112","author":"Zhou","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ardiansyah, M.F., William, T., Abdullaziz, O.I., Wang, L.-C., Tien, P.-L., and Yuang, M.C. (2020, January 15\u201318). EagleEYE: Aerial edge-enabled disaster relief response system. Proceedings of the 2020 European Conference on Networks and Communications (EuCNC), Dubrovnik, Croatia.","DOI":"10.1109\/EuCNC48522.2020.9200963"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhang, D.Y., Zhang, Y., Li, Q., Plummer, T., and Wang, D. (2019, January 7\u201310). CrowdLearn: A Crowd-AI hybrid system for deep learning-based damage assessment applications. Proceedings of the 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Dallas, TX, USA.","DOI":"10.1109\/ICDCS.2019.00123"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Alam, F., Imran, M., and Ofli, F. (August, January 31). Image4Act: Online social media image processing for disaster response. Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia.","DOI":"10.1145\/3110025.3110164"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3057280","article-title":"DeepMob: Learning Deep Knowledge of Human Emergency Behaviour and Mobility from Big and Heterogeneous Data","volume":"35","author":"Song","year":"2017","journal-title":"ACM Transac. Inf. Sys."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.inffus.2021.10.007","article-title":"Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence","volume":"79","author":"Holzinger","year":"2022","journal-title":"Inf. Fusion"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/20\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:07:32Z","timestamp":1760137652000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,7]]},"references-count":73,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["make4020020"],"URL":"https:\/\/doi.org\/10.3390\/make4020020","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,7]]}}}