{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T18:40:07Z","timestamp":1750531207631,"version":"3.41.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031961953","type":"print"},{"value":"9783031961960","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\/978-3-031-96196-0_20","type":"book-chapter","created":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T18:13:43Z","timestamp":1750529623000},"page":"267-280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Utilizing Multiple Data Sources to\u00a0Improve Prediction of\u00a0Severe Weather Events Through Spatio-Temporal Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2745-5357","authenticated-orcid":false,"given":"Hussain","family":"Otudi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5425-4491","authenticated-orcid":false,"given":"Shelly","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5173-5291","authenticated-orcid":false,"given":"Ameen Abdel","family":"Hai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-3772","authenticated-orcid":false,"given":"Rafaa","family":"Aljurbua","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7559-3533","authenticated-orcid":false,"given":"Abdulrahman","family":"Alharbi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2051-0142","authenticated-orcid":false,"given":"Zoran","family":"Obradovic","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,22]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Aljurbua, R., Alshehri, J., Gupta, S., Alharbi, A., Obradovic, Z.: Early prediction of power outage duration through hierarchical spatiotemporal multiplex networks. In: Proceedings of 13th International Conference on Complex Networks and their Applications. Springer Verlag, Istanbul, Turkey (2024), (in Press)","DOI":"10.1007\/978-3-031-82435-7_26"},{"issue":"2","key":"20_CR2","first-page":"1234","volume":"38","author":"M Alqudah","year":"2023","unstructured":"Alqudah, M.: Others: power system measurements for severe weather prediction: a multi-instance learning approach. IEEE Trans. Power Syst. 38(2), 1234\u20131245 (2023)","journal-title":"IEEE Trans. Power Syst."},{"key":"20_CR3","first-page":"108789","volume":"215","author":"S Arora","year":"2023","unstructured":"Arora, S., Chen, L.: Challenges in extreme event prediction for power systems. Electric Power Syst. Res. 215, 108789 (2023)","journal-title":"Electric Power Syst. Res."},{"issue":"4","key":"20_CR4","first-page":"3055","volume":"35","author":"D Cerrai","year":"2020","unstructured":"Cerrai, D., Anagnostou, M.A., Anagnostou, E.N.: Machine learning models for predicting power outages caused by extreme weather events. IEEE Trans. Power Syst. 35(4), 3055\u20133066 (2020)","journal-title":"IEEE Trans. Power Syst."},{"issue":"3","key":"20_CR5","first-page":"04019015","volume":"25","author":"K Dokic","year":"2019","unstructured":"Dokic, K., Texas, A.: Big data analytics and spatiotemporal modeling for assessing weather impacts on utility assets. J. Infrastruct. Syst. 25(3), 04019015 (2019)","journal-title":"J. Infrastruct. Syst."},{"key":"20_CR6","unstructured":"Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01), pp. 973\u2013978 (2001)"},{"key":"20_CR7","first-page":"178","volume":"9","author":"Z Fatima","year":"2024","unstructured":"Fatima, Z., Liu, R., Zhang, H.: Multimodal spatiotemporal framework for hurricane-induced outage prediction. Nat. Energy 9, 178\u2013189 (2024)","journal-title":"Nat. Energy"},{"issue":"8","key":"20_CR8","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"issue":"6","key":"20_CR9","first-page":"2101","volume":"35","author":"ML Flora","year":"2020","unstructured":"Flora, M.L., Skinner, P., Stensrud, D.: Short-term severe weather forecasting using the warn-on-forecast system and random forest models. Weather Forecast. 35(6), 2101\u20132115 (2020)","journal-title":"Weather Forecast."},{"issue":"10","key":"20_CR10","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1111\/j.1539-6924.2009.01280.x","volume":"29","author":"SR Han","year":"2009","unstructured":"Han, S.R., Guikema, S.D., Quiring, S.M.: Improving the accuracy of hurricane-related power outage forecasts using generalized additive models. Risk Anal. 29(10), 1443\u20131453 (2009)","journal-title":"Risk Anal."},{"issue":"3","key":"20_CR11","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1175\/MWR-D-21-0222.1","volume":"150","author":"AJ Hill","year":"2022","unstructured":"Hill, A.J., Gagne, D.J., McGovern, A.: Calibration and reliability of random forest-based severe weather forecasts. Mon. Weather Rev. 150(3), 567\u2013582 (2022)","journal-title":"Mon. Weather Rev."},{"issue":"5","key":"20_CR12","first-page":"1587","volume":"35","author":"AJ Hill","year":"2020","unstructured":"Hill, A.J., Gagne, D.J., McGovern, A.: Random forest models for predicting severe weather events across the contiguous united states. Weather Forecast. 35(5), 1587\u20131603 (2020)","journal-title":"Weather Forecast."},{"issue":"3","key":"20_CR13","first-page":"275","volume":"73","author":"NP Kettle","year":"2020","unstructured":"Kettle, N.P., Gladden, L., Martin, J.: Climate adaptation planning in alaska native villages: a systematic review. Arctic 73(3), 275\u2013291 (2020)","journal-title":"Arctic"},{"key":"20_CR14","first-page":"384","volume":"215","author":"NP Kettle","year":"2018","unstructured":"Kettle, N.P., Martin, J., Sloan, M.: Building resilience to natural hazards through knowledge networks in alaska native villages. J. Environ. Manage. 215, 384\u2013394 (2018)","journal-title":"J. Environ. Manage."},{"key":"20_CR15","unstructured":"Li, E.A.: Urbangpt: integrating spatio-temporal dependencies for urban prediction. J. Urban Intell. (2024)"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Ling, C.X., Sheng, V.S.: Cost-sensitive learning and the class imbalance problem. Encyclopedia Mach. Learn. 231\u2013235 (2010)","DOI":"10.1007\/978-0-387-30164-8_181"},{"issue":"10","key":"20_CR17","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","volume":"40","author":"J Lipkova","year":"2022","unstructured":"Lipkova, J., et al.: Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40(10), 1095\u20131110 (2022)","journal-title":"Cancer Cell"},{"key":"20_CR18","unstructured":"Liu, E.A.: St-llm: specialized spatio-temporal prediction in urban settings. IEEE Trans. Intell. Transp. Syst. (2024)"},{"issue":"12","key":"20_CR19","first-page":"4321","volume":"147","author":"ED Loken","year":"2019","unstructured":"Loken, E.D., Clark, A.J., Gagne, D.J.: Calibrating probabilistic precipitation forecasts using random forest models. Mon. Weather Rev. 147(12), 4321\u20134336 (2019)","journal-title":"Mon. Weather Rev."},{"issue":"2","key":"20_CR20","first-page":"147","volume":"91","author":"A McGovern","year":"2013","unstructured":"McGovern, A.: Others: spatiotemporal relational learning for severe weather prediction. Mach. Learn. 91(2), 147\u2013180 (2013)","journal-title":"Mach. Learn."},{"key":"20_CR21","unstructured":"National Oceanic and Atmospheric Administration: Storm events database. https:\/\/www.ncdc.noaa.gov\/stormevents\/ (2023)"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Otudi, H., Gupta, S., Albarakati, N., Obradovic, Z.: Classifying severe weather events by utilizing social sensor data and social network analysis. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pp. 64\u201371, November 2023","DOI":"10.1145\/3625007.3627298"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Otudi, H., Gupta, S., Obradovic, Z.: Leveraging diverse data sources for enhanced prediction of severe weather-related disruptions across different time horizons. In: International Conference on Engineering Applications of Neural Networks, pp. 220\u2013234 (2024)","DOI":"10.1007\/978-3-031-62495-7_17"},{"issue":"3","key":"20_CR24","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1023\/A:1007601015854","volume":"42","author":"F Provost","year":"2001","unstructured":"Provost, F., Fawcett, T.: Robust classification for imprecise environments. Mach. Learn. 42(3), 203\u2013231 (2001)","journal-title":"Mach. Learn."},{"issue":"4","key":"20_CR25","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1163\/18786561-00704003","volume":"7","author":"E Ristroph","year":"2018","unstructured":"Ristroph, E.: When climate takes a village: legal pathways toward the relocation of alaska native villages. Climate Law 7(4), 259\u2013289 (2018)","journal-title":"Climate Law"},{"key":"20_CR26","first-page":"100456","volume":"33","author":"JE Walsh","year":"2024","unstructured":"Walsh, J.E., Thoman, R.L., Bhatt, U.S.: Alaska climate change assessment: recent changes and future projections. Weather Clim. Extremes 33, 100456 (2024)","journal-title":"Weather Clim. Extremes"},{"key":"20_CR27","first-page":"3214","volume":"37","author":"JP Watson","year":"2022","unstructured":"Watson, J.P., Guttromson, R.: Uncertainty quantification in power system resilience. IEEE Trans. Power Syst. 37, 3214\u20133226 (2022)","journal-title":"IEEE Trans. Power Syst."},{"issue":"5","key":"20_CR28","first-page":"987","volume":"42","author":"X Yue","year":"2022","unstructured":"Yue, X., Guikema, S.D., Nateghi, R.: Problem reformulation to improve regression analysis in granular damage forecasting. Risk Anal. 42(5), 987\u20131001 (2022)","journal-title":"Risk Anal."},{"issue":"3","key":"20_CR29","first-page":"45","volume":"15","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y.: Advances in event prediction: leveraging artificial intelligence and big data. J. Predictive Anal. 15(3), 45\u201360 (2020)","journal-title":"J. Predictive Anal."}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96196-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T18:13:48Z","timestamp":1750529628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96196-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031961953","9783031961960"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96196-0_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"22 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Limassol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cyprus","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2025a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eannconf.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}