{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:12:27Z","timestamp":1784290347385,"version":"3.55.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031790379","type":"print"},{"value":"9783031790386","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-79038-6_11","type":"book-chapter","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T06:17:38Z","timestamp":1738217858000},"page":"152-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainability of\u00a0Machine Learning Models with\u00a0XGBoost and\u00a0SHAP Values in\u00a0the\u00a0Context of\u00a0Coping with\u00a0Disasters"],"prefix":"10.1007","author":[{"given":"Lucas","family":"Teixeira","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Augusto","family":"Matos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel","family":"Carvalho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norma","family":"Valencio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heloisa","family":"Camargo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"11_CR1","unstructured":"Brasil: Classifica\u00e7\u00e3o e Codifica\u00e7\u00e3o Brasileira de Desastres. Secretaria Nacional de Prote\u00e7\u00e3o e Defesa Civil-SEDEC\/MDR, Bras\u00edlia (2012)"},{"key":"11_CR2","unstructured":"Kowarick, L.: Viver em risco: sobre a vulnerabilidade socioecon\u00f4mica e civil. Editora 34, S\u00e3o Paulo (2009)"},{"key":"11_CR3","unstructured":"Fritz, C.: Disaster. In: Merton, R.. Nisbet, R. (eds.) Contemporary Social Problems. 1$$^{\\underline{a}}$$ ed., pp. 651\u2013694. Harcourt Brace Jovanovich, New York (1961)"},{"key":"11_CR4","unstructured":"Quarantelli, E.: Uma agenda de pesquisa do s\u00e9culo 21 em ci\u00eancias sociais para os desastres: quest\u00f5es te\u00f3ricas, metodol\u00f3gicas e emp\u00edricas, e suas implementa\u00e7\u00f5es no campo professional. O Social em Quest\u00e3o. Rio de Janeiro, vol. 18, pp. 25\u201356 (2015)"},{"key":"11_CR5","unstructured":"Valencio, N.: Para al\u00e9m do \u2018dia do desastre\u2019: o caso brasileiro. Cole\u00e7\u00e3o Ci\u00eancias Sociais. Ed. Appris, Curitiba (2012)"},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"19","DOI":"10.34019\/1981-4070.2018.v12.21531","volume":"12","author":"N Valencio","year":"2018","unstructured":"Valencio, N., Valencio, A.: O ass\u00e9dio em nome do bem: Dos sofrimentos conectados \u00e0 dor moral coletiva de v\u00edtimas de desastres. LUMINA 12, 19\u201339 (2018)","journal-title":"LUMINA"},{"key":"11_CR7","unstructured":"United Nations: The Sustainable Development Goals Report 2023, Special United Nations Publications, New York (2023)"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Arrieta","year":"2020","unstructured":"Arrieta, A., et al.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"11_CR9","first-page":"4765","volume":"30","author":"S Lundberg","year":"2017","unstructured":"Lundberg, S., Lee, S.-I.: A unified approach to interpreting model predictions. Adv. Neural. Inf. Process. Syst. 30, 4765\u20134774 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. ACM, New York (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"137","key":"11_CR12","first-page":"1","volume":"23","author":"F Yi","year":"2023","unstructured":"Yi, F., Yang, H., Chen, D., et al.: XGBoost-SHAP-based interpretable diagnostic framework for alzheimer\u2019s disease. BMC Med. Inform. Decis. Mak. 23(137), 1\u201314 (2023)","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"11_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120375","volume":"227","author":"B Tan","year":"2023","unstructured":"Tan, B., Gan, Z., Wu, Y.: The measurement and early warning of daily financial stability index based on XGBoost and SHAP: evidence from China. Expert Syst. Appl. 227, 120325 (2023)","journal-title":"Expert Syst. Appl."},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"12649","DOI":"10.1109\/ACCESS.2023.3241627","volume":"11","author":"F Hatami","year":"2023","unstructured":"Hatami, F., Rahman, M., Nikparvar, B., Thill, J.-C.: Non-linear associations between the urban built environment and commuting modal split: a random forest approach and SHAP evaluation. IEEE Access 11, 12649\u201312662 (2023)","journal-title":"IEEE Access"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Andrade, J., De Souza Junior, T., Silva, L. Lucena, D., Fernandes, B.: Assessing the effect of urban expansion and deforestation on temperature rise in Cajazeiras, Brazil: a data-driven approach. In: 2023 IEEE LA-CCI, pp. 1\u20136. IEEE, Recife (2023)","DOI":"10.1109\/LA-CCI58595.2023.10409431"},{"key":"11_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2023.117357","volume":"332","author":"J Zhang","year":"2023","unstructured":"Zhang, J., et al.: Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. J. Environ. Manage. 332, 117357 (2023)","journal-title":"J. Environ. Manage."},{"key":"11_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2024.102601","volume":"81","author":"L Van","year":"2024","unstructured":"Van, L., Tran, V., Nguyen, G., Yeon, M., Do, M., Lee, G.: Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: a novel approach through qualitative and quantitative feature selection with explainable AI. Eco. Inform. 81, 102601 (2024)","journal-title":"Eco. Inform."},{"key":"11_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.165509","volume":"898","author":"B Zhang","year":"2023","unstructured":"Zhang, B., Salem, F., Hayes, M., Smith, K., Tadesse, T., Wardlow, B.: Explainable machine learning for the prediction and assessment of complex drought impacts. Sci. Total Environ. 898, 165509 (2023)","journal-title":"Sci. Total Environ."},{"key":"11_CR19","doi-asserted-by":"publisher","first-page":"12261","DOI":"10.3390\/su151612261","volume":"15","author":"S Liu","year":"2023","unstructured":"Liu, S., et al.: Evaluation of tropical cyclone disaster loss using machine learning algorithms with an explainable artificial intelligence approach. Sustainability 15, 12261 (2023)","journal-title":"Sustainability"},{"key":"11_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2023.111137","volume":"156","author":"M Wang","year":"2023","unstructured":"Wang, M., et al.: An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility. Ecol. Ind. 156, 111137 (2023)","journal-title":"Ecol. Ind."},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Highland, L.M., Bobrowsky, P.: The landslide handbook\u2014a guide to understanding landslides. U.S. Geological Survey Circular, vol. 1325 (2008)","DOI":"10.3133\/cir1325"},{"issue":"1","key":"11_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"X Wu","year":"2008","unstructured":"Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1\u201337 (2008)","journal-title":"Knowl. Inf. Syst."}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-79038-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T06:17:53Z","timestamp":1738217873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-79038-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031790379","9783031790386"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-79038-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"31 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bel\u00e9m do Par\u00e1","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}