--- _id: '29037' abstract: - lang: eng text: Existing technologies employ different machine learning approaches to predict disasters from historical environmental data. However, for short-term disasters (e.g., earthquakes), historical data alone has a limited prediction capability. In this work, we consider social media as a supplementary source of knowledge in addition to historical environmental data. Further, we build a joint model that learns from disaster-related tweets and environmental data to improve prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with their semantics representations computed with the traditional word2vec. Our experiments show that our proposed approach outperforms the accuracy of state-of-the-art models in disaster prediction. author: - first_name: Hamada Mohamed Abdelsamee full_name: Zahera, Hamada Mohamed Abdelsamee id: '72768' last_name: Zahera orcid: 0000-0003-0215-1278 - first_name: Mohamed full_name: Sherif, Mohamed id: '67234' last_name: Sherif orcid: https://orcid.org/0000-0002-9927-2203 - first_name: Axel-Cyrille full_name: Ngonga Ngomo, Axel-Cyrille id: '65716' last_name: Ngonga Ngomo citation: ama: 'Zahera HMA, Sherif M, Ngonga Ngomo A-C. Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. In: K-CAP 2019: Knowledge Capture Conference. ; 2019:4.' apa: 'Zahera, H. M. A., Sherif, M., & Ngonga Ngomo, A.-C. (2019). Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. K-CAP 2019: Knowledge Capture Conference, 4.' bibtex: '@inproceedings{Zahera_Sherif_Ngonga Ngomo_2019, title={Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction}, booktitle={K-CAP 2019: Knowledge Capture Conference}, author={Zahera, Hamada Mohamed Abdelsamee and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2019}, pages={4} }' chicago: 'Zahera, Hamada Mohamed Abdelsamee, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction.” In K-CAP 2019: Knowledge Capture Conference, 4, 2019.' ieee: 'H. M. A. Zahera, M. Sherif, and A.-C. Ngonga Ngomo, “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction,” in K-CAP 2019: Knowledge Capture Conference, 2019, p. 4.' mla: 'Zahera, Hamada Mohamed Abdelsamee, et al. “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction.” K-CAP 2019: Knowledge Capture Conference, 2019, p. 4.' short: 'H.M.A. Zahera, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge Capture Conference, 2019, p. 4.' date_created: 2021-12-17T10:05:07Z date_updated: 2023-08-16T09:24:21Z keyword: - sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba ngonga simba zahera sherif solide limboproject opal group\_aksw dice language: - iso: eng page: '4' publication: 'K-CAP 2019: Knowledge Capture Conference' status: public title: Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction type: conference user_id: '67234' year: '2019' ...