{"title":"Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction","date_updated":"2023-08-16T09:24:21Z","type":"conference","user_id":"67234","page":"4","status":"public","author":[{"full_name":"Zahera, Hamada Mohamed Abdelsamee","orcid":"0000-0003-0215-1278","first_name":"Hamada Mohamed Abdelsamee","last_name":"Zahera","id":"72768"},{"full_name":"Sherif, Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","id":"67234","last_name":"Sherif","first_name":"Mohamed"},{"id":"65716","first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille"}],"date_created":"2021-12-17T10:05:07Z","publication":"K-CAP 2019: Knowledge Capture Conference","year":"2019","_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."}],"language":[{"iso":"eng"}],"citation":{"ama":"Zahera HMA, Sherif M, Ngonga Ngomo A-C. Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. In: <i>K-CAP 2019: Knowledge Capture Conference</i>. ; 2019:4.","apa":"Zahera, H. M. A., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2019). Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction. <i>K-CAP 2019: Knowledge Capture Conference</i>, 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 <i>K-CAP 2019: Knowledge Capture Conference</i>, 4, 2019.","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction.” <i>K-CAP 2019: Knowledge Capture Conference</i>, 2019, p. 4.","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 <i>K-CAP 2019: Knowledge Capture Conference</i>, 2019, p. 4.","short":"H.M.A. Zahera, M. Sherif, A.-C. Ngonga Ngomo, in: K-CAP 2019: Knowledge Capture Conference, 2019, p. 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} }"},"keyword":["sys:relevantFor:infai sys:relevantFor:bis sys:relevantFor:simba ngonga simba zahera sherif solide limboproject opal group\\_aksw dice"]}