short-paper

Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction

Authors:

Hamada M. Zahera,

Mohamed Ahmed Sherif,

Axel-Cyrille Ngonga NgomoAuthors Info & Claims

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

Pages 231 - 234

https://doi.org/10.1145/3360901.3364413

Published: 23 September 2019 Publication History

Abstract

Existing technologies employ different machine learning approachesto predict disasters from historical environmental data. However,for short-term disasters (e.g., earthquakes), historical data alonehas a limited prediction capability. In this work, we consider so-cial media as a supplementary source of knowledge in additionto historical environmental data. Further, we build a joint modelthat learns from disaster-related tweets and environmental data toimprove prediction. We propose the combination of semantically-enriched word embedding to represent entities in tweets with theirsemantics representations computed with the traditionalword2vec.Our experiments show that our proposed approach outperformsthe accuracy of state-of-the-art models in disaster prediction

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Cited By

Yang WFei JHuang XDing JCheng X(2024)Enhancing Tropical Cyclone Intensity Estimation from Satellite Imagery through Deep Learning TechniquesJournal of Meteorological Research10.1007/s13351-024-3186-y38:4(652-663)Online publication date: 6-Sep-2024
https://doi.org/10.1007/s13351-024-3186-y
Zhou GXu JQian QXu YXu Y(2022)Discriminating Technique of Typhoon Rapid Intensification Trend Based on Artificial IntelligenceAtmosphere10.3390/atmos1303044813:3(448)Online publication date: 10-Mar-2022
https://doi.org/10.3390/atmos13030448
SEBATLI SAĞLAM AÇAVDUR F(2022)Yapay sinir ağı ile deprem şiddeti tahmini: Farklı ağ tasarımlarının ve eğitim algoritmalarının incelenmesiEarthquake intensity estimation via an artificial neural network: Examination of different network designs and training algorithmsGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.79133737:4(2133-2146)Online publication date: 28-Feb-2022
https://doi.org/10.17341/gazimmfd.791337
Show More Cited By

Index Terms

Jointly Learning from Social Media and Environmental Data for Typhoon Intensity Prediction
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. World Wide Web
    1. Web mining

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Information

Published In

cover image ACM Conferences

K-CAP '19: Proceedings of the 10th International Conference on Knowledge Capture

September 2019

281 pages

ISBN:9781450370080

DOI:10.1145/3360901

General Chairs:
Mayank Kejriwal
University of Southern California Information Sciences Institute, USA
,
Pedro Szekely
University of Southern California Information Sciences Institute, USA
,
Program Chair:
Raphaël Troncy
EURECOM, France

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2019

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Short-paper

Funding Sources

Bundesministerium für Verkehr und Digitale Infrastruktur
Eurostars
Bundesministerium für Bildung und Forschung

Conference

K-CAP '19

Sponsor:

SIGAI

K-CAP '19: Knowledge Capture Conference

November 19 - 21, 2019

CA, Marina Del Rey, USA

Acceptance Rates

Overall Acceptance Rate 55 of 198 submissions, 28%

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Citations

Cited By

Yang WFei JHuang XDing JCheng X(2024)Enhancing Tropical Cyclone Intensity Estimation from Satellite Imagery through Deep Learning TechniquesJournal of Meteorological Research10.1007/s13351-024-3186-y38:4(652-663)Online publication date: 6-Sep-2024
https://doi.org/10.1007/s13351-024-3186-y
Zhou GXu JQian QXu YXu Y(2022)Discriminating Technique of Typhoon Rapid Intensification Trend Based on Artificial IntelligenceAtmosphere10.3390/atmos1303044813:3(448)Online publication date: 10-Mar-2022
https://doi.org/10.3390/atmos13030448
SEBATLI SAĞLAM AÇAVDUR F(2022)Yapay sinir ağı ile deprem şiddeti tahmini: Farklı ağ tasarımlarının ve eğitim algoritmalarının incelenmesiEarthquake intensity estimation via an artificial neural network: Examination of different network designs and training algorithmsGazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi10.17341/gazimmfd.79133737:4(2133-2146)Online publication date: 28-Feb-2022
https://doi.org/10.17341/gazimmfd.791337
Zhang ZYang XWang XWang BWang CDu Z(2022)A neural network with spatiotemporal encoding module for tropical cyclone intensity estimation from infrared satellite imageKnowledge-Based Systems10.1016/j.knosys.2022.110005258:COnline publication date: 22-Dec-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.110005
Zhang ZYang XShi LWang BDu ZZhang FLiu R(2022)A neural network framework for fine-grained tropical cyclone intensity predictionKnowledge-Based Systems10.1016/j.knosys.2022.108195241:COnline publication date: 6-Apr-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108195

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