---
_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'
...