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.
%0 Conference Paper
%1 JointModel2019
%A Zahera, Hamada M.
%A Sherif, Mohamed Ahmed
%A Ngonga Ngomo, Axel-Cyrille
%B K-CAP 2019: Knowledge Capture Conference
%D 2019
%K dice group\_aksw limboproject ngonga opal sherif simba solide zahera
%P 4
%T Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction
%U https://papers.dice-research.org/2019/K-CAP19_Joint-model/K-CAP19_Joint-model-public.pdf
%X 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.
@inproceedings{JointModel2019,
abstract = {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.},
added-at = {2020-06-18T14:14:56.000+0200},
author = {Zahera, Hamada M. and Sherif, Mohamed Ahmed and {Ngonga Ngomo}, Axel-Cyrille},
bdsk-url-1 = {https://papers.dice-research.org/2019/K-CAP19_Joint-model/K-CAP19_Joint-model-public.pdf},
biburl = {https://www.bibsonomy.org/bibtex/275f99a868d0a21313f8cbae23c5ec1a0/dice-research},
booktitle = {K-CAP 2019: Knowledge Capture Conference},
interhash = {03fa7b1612f9a3a7b850bdd4033d4823},
intrahash = {75f99a868d0a21313f8cbae23c5ec1a0},
keywords = {dice group\_aksw limboproject ngonga opal sherif simba solide zahera},
organization = {ACM},
pages = 4,
timestamp = {2024-09-18T16:18:30.000+0200},
title = {Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction},
url = {https://papers.dice-research.org/2019/K-CAP19_Joint-model/K-CAP19_Joint-model-public.pdf},
year = 2019
}