{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T18:09:49Z","timestamp":1750874989958,"version":"3.41.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031961984","type":"print"},{"value":"9783031961991","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\/978-3-031-96199-1_13","type":"book-chapter","created":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T18:07:25Z","timestamp":1750529245000},"page":"165-178","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatiotemporal Multiplex Network Model for\u00a0Predicting Forced Outage Severity in\u00a0Distribution Grids"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-3772","authenticated-orcid":false,"given":"Rafaa","family":"Aljurbua","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6515-8007","authenticated-orcid":false,"given":"Rashid","family":"Baembitov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2535-2179","authenticated-orcid":false,"given":"Christos","family":"Petridis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6003-2050","authenticated-orcid":false,"given":"Daniel","family":"Saranovic","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8925-4415","authenticated-orcid":false,"given":"Mladen","family":"Kezunovic","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":"13_CR1","doi-asserted-by":"crossref","unstructured":"Aljurbua, R., Alshehri, J., Alharbi, A., Power, W., Obradovic, Z.: Social media sensors for weather-caused outage prediction based on spatio-temporal multiplex network representation. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3327444"},{"key":"13_CR2","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: Complex Networks & Their Applications XIII: Proceedings of The Thirteenth International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2024, vol. 3, p.\u00a0320. Springer (2024)","DOI":"10.1007\/978-3-031-82435-7_26"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Aljurbua, R., Gillespie, A., Alshehri, J., Alharbi, A., Albarakati, N., Obradovic, Z.: Node2vecfuseclassifier: bridging perspectives in modeling transplantation attitudes among dialysis patients. In: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp. 113\u2013122. IEEE (2024)","DOI":"10.1109\/ICHI61247.2024.00022"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Aljurbua, R., Gillespie, A., Obradovic, Z.: The company we keep. Using hemodialysis social network data to classify patients\u2019 kidney transplant attitudes with machine learning algorithms. BMC Nephrol. 23(1), 414 (2022)","DOI":"10.1186\/s12882-022-03049-2"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Alqudah, M., Obradovic, Z.: Enhancing weather-related outage prediction and precursor discovery through attention-based multi-level modeling. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3303110"},{"key":"13_CR6","unstructured":"Automated Surface Observing Systems: NOAA\u2019s National Weather Service. https:\/\/www.weather.gov\/asos\/asostech. Accessed Aug 2024"},{"key":"13_CR7","doi-asserted-by":"publisher","first-page":"98654","DOI":"10.1109\/ACCESS.2021.3093547","volume":"9","author":"S Azad","year":"2021","unstructured":"Azad, S., Ghandehari, M.: A study on the association of socioeconomic and physical cofactors contributing to power restoration after hurricane maria. IEEE Access 9, 98654\u201398664 (2021)","journal-title":"IEEE Access"},{"key":"13_CR8","unstructured":"Baembitov, R., Karmacharya, A., Kezunovic, M., Saranovic, D., Obradovic, Z.: Effect of lightning features on predicting outages related to thunderstorms in distribution grids. In: Proceedings of the 58th IEEE Hawaii International Conference on System Science (HICSS), pp. 2978\u20132987. IEEE (2025)"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Baembitov, R., Kezunovic, M., Saranovic, D., Obradovic, Z.: Sensitivity analysis of machine learning algorithms for outage risk prediction. In: Proceedings of the 57th IEEE Hawaii International Conference on System Science (HICSS), pp. 3150\u20133159. IEEE (2024)","DOI":"10.24251\/HICSS.2024.380"},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ijepes.2013.02.008","volume":"50","author":"E Bompard","year":"2013","unstructured":"Bompard, E., Huang, T., Wu, Y., Cremenescu, M.: Classification and trend analysis of threats origins to the security of power systems. Int. J. Electr. Power Energy Syst. 50, 50\u201364 (2013)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Dokic, T., Pavlovski, M.: Spatially aware ensemble-based learning to predict weather-related outages in transmission. In: The Hawaii International Conference on System Sciences\u2013HICSS, Maui, Hawaii, January 2019 (2019)","DOI":"10.24251\/HICSS.2019.422"},{"issue":"4","key":"13_CR12","doi-asserted-by":"publisher","first-page":"3315","DOI":"10.1109\/TPWRS.2016.2631895","volume":"32","author":"R Eskandarpour","year":"2016","unstructured":"Eskandarpour, R., Khodaei, A.: Machine learning based power grid outage prediction in response to extreme events. IEEE Trans. Power Syst. 32(4), 3315\u20133316 (2016)","journal-title":"IEEE Trans. Power Syst."},{"issue":"3","key":"13_CR13","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/MSPEC.1978.6369445","volume":"15","author":"LH Fink","year":"1978","unstructured":"Fink, L.H., Carlsen, K.: Operating under stress and strain [electrical power systems control under emergency conditions]. IEEE Spectr. 15(3), 48\u201353 (1978)","journal-title":"IEEE Spectr."},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"13_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109398","volume":"237","author":"H Hou","year":"2023","unstructured":"Hou, H., Liu, C., Wei, R., He, H., Wang, L., Li, W.: Outage duration prediction under typhoon disaster with stacking ensemble learning. Reliab. Eng. Syst. Safety 237, 109398 (2023)","journal-title":"Reliab. Eng. Syst. Safety"},{"key":"13_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014), https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Lind, P.G., Gonzalez, M.C., Herrmann, H.J.: Cycles and clustering in bipartite networks. Phys. Rev. E\u2014Stat. Nonlin. Soft Matter Phys. 72(5), 056127 (2005)","DOI":"10.1103\/PhysRevE.72.056127"},{"issue":"4","key":"13_CR18","doi-asserted-by":"publisher","first-page":"2270","DOI":"10.1109\/TPWRS.2007.907587","volume":"22","author":"H Liu","year":"2007","unstructured":"Liu, H., Davidson, R.A., Apanasovich, T.V.: Statistical forecasting of electric power restoration times in hurricanes and ice storms. IEEE Trans. Power Syst. 22(4), 2270\u20132279 (2007)","journal-title":"IEEE Trans. Power Syst."},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Mensah, A.F., Due\u00f1as-Osorio, L.: Outage predictions of electric power systems under hurricane winds by bayesian networks. In: 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp.\u00a01\u20136. IEEE (2014)","DOI":"10.1109\/PMAPS.2014.6960677"},{"issue":"2","key":"13_CR20","doi-asserted-by":"publisher","first-page":"516","DOI":"10.3390\/su11020516","volume":"11","author":"D Mitsova","year":"2019","unstructured":"Mitsova, D., Escaleras, M., Sapat, A., Esnard, A.M., Lamadrid, A.J.: The effects of infrastructure service disruptions and socio-economic vulnerability on hurricane recovery. Sustainability 11(2), 516 (2019)","journal-title":"Sustainability"},{"issue":"12","key":"13_CR21","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.1111\/j.1539-6924.2011.01618.x","volume":"31","author":"R Nateghi","year":"2011","unstructured":"Nateghi, R., Guikema, S.D., Quiring, S.M.: Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes. Risk Anal. Int. J. 31(12), 1897\u20131906 (2011)","journal-title":"Risk Anal. Int. J."},{"issue":"3","key":"13_CR22","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1007\/s11069-014-1270-9","volume":"74","author":"R Nateghi","year":"2014","unstructured":"Nateghi, R., Guikema, S.D., Quiring, S.M.: Forecasting hurricane-induced power outage durations. Nat. Hazards 74(3), 1795\u20131811 (2014). https:\/\/doi.org\/10.1007\/s11069-014-1270-9","journal-title":"Nat. Hazards"},{"key":"13_CR23","unstructured":"Open-Meteo: Open-meteo. https:\/\/open-meteo.com\/. Accessed Sep 2024"},{"key":"13_CR24","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"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Owerko, D., Gama, F., Ribeiro, A.: Predicting power outages using graph neural networks. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 743\u2013747. IEEE (2018)","DOI":"10.1109\/GlobalSIP.2018.8646486"},{"issue":"1","key":"13_CR26","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.jweia.2007.04.002","volume":"96","author":"DA Reed","year":"2008","unstructured":"Reed, D.A.: Electric utility distribution analysis for extreme winds. J. Wind Eng. Ind. Aerodyn. 96(1), 123\u2013140 (2008)","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"issue":"1","key":"13_CR27","doi-asserted-by":"publisher","first-page":"38","DOI":"10.2307\/2309088","volume":"64","author":"CC Robusto","year":"1957","unstructured":"Robusto, C.C.: The cosine-haversine formula. Am. Math. Mon. 64(1), 38\u201340 (1957)","journal-title":"Am. Math. Mon."},{"issue":"1","key":"13_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-020-00331-w","volume":"5","author":"B \u0160krlj","year":"2020","unstructured":"\u0160krlj, B., Renoust, B.: Layer entanglement in multiplex, temporal multiplex, and coupled multilayer networks. Appl. Netw. Sci. 5(1), 1\u201334 (2020). https:\/\/doi.org\/10.1007\/s41109-020-00331-w","journal-title":"Appl. Netw. Sci."},{"key":"13_CR29","unstructured":"Texas Parks and Wildlife Department: Ecological Mapping Systems. https:\/\/tpwd.texas.gov\/landwater\/land\/programs\/landscape-ecology\/ems\/. Accessed Aug 2024"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Wong, C.J., Miller, M.D.: Guidelines for electrical transmission line structural loading. American Society of Civil Engineers (2009)","DOI":"10.1061\/9780784410356"},{"issue":"4","key":"13_CR31","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.3390\/su12041525","volume":"12","author":"F Yang","year":"2020","unstructured":"Yang, F., Wanik, D.W., Cerrai, D., Bhuiyan, M., Anagnostou, E.N.: Quantifying uncertainty in machine learning-based power outage prediction model training: a tool for sustainable storm restoration. Sustainability 12(4), 1525 (2020)","journal-title":"Sustainability"}],"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\/978-3-031-96199-1_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T18:07:30Z","timestamp":1750529250000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96199-1_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031961984","9783031961991"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96199-1_13","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"}}]}}