research-article

Classification of Inundation Level using Tweets in Indonesian Language

Authors:

Kwee Felicia Ilona,

Indra BudiAuthors Info & Claims

ICSCA '21: Proceedings of the 2021 10th International Conference on Software and Computer Applications

Pages 137 - 143

https://doi.org/10.1145/3457784.3457806

Published: 30 July 2021 Publication History

Abstract

Extreme flood events are expected to occur more frequently as climate change has yet to show signs of improvement. This has the potential to lead to higher rainfall and floods that would come more quickly. Early warning systems may sometimes fail to provide quick information when conditions in the field may not match to what is known in the information center, such as a malfunctioning water pump or a water level that has increased relatively quickly. Therefore, this study aims to provide an alternative source of information that may provide inundation level during flood condition based on tweets from Twitter. The proposed model is expected to provide output in the form of inundation level categories, namely “high”, “medium”, “low”, and “unknown”. 10-fold stratified cross validation with seven variations of classifiers were used to evaluate the model. The best relevance classification resulted in 90.6% accuracy (SVM Linear SVC), 89.05% average precision (SVM RBF), and 82.03% average F1-score (SVM Linear SVC) and average recall of 84.10% (Logistic Regression). The best classification results of inundation level resulted in accuracy (82.74%), average precision (85.44%) average recall (68.07%) and average F1-score (71.43%). All of them were obtained by using the SVM Linear SVC.

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Cited By

Moriyani MAsaye LLe CLe TLe T(2024)Natural Language Processing for Infrastructure Resilience to Natural Disasters: A Scientometric ReviewProceedings of the 7th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2024, 4-5 April, Ho Chi Minh City, Vietnam10.1007/978-981-97-1972-3_165(1506-1513)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-981-97-1972-3_165
Thakur N(2022)Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research QuestionsAnalytics10.3390/analytics10200071:2(72-97)Online publication date: 23-Sep-2022
https://doi.org/10.3390/analytics1020007
Thakur N(undefined)Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research QuestionsSSRN Electronic Journal10.2139/ssrn.4170991
https://doi.org/10.2139/ssrn.4170991

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cover image ACM Other conferences

ICSCA '21: Proceedings of the 2021 10th International Conference on Software and Computer Applications

February 2021

325 pages

ISBN:9781450388825

DOI:10.1145/3457784

Copyright © 2021 ACM.

© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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New York, NY, United States

Publication History

Published: 30 July 2021

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ICSCA 2021: 2021 10th International Conference on Software and Computer Applications

February 23 - 26, 2021

Kuala Lumpur, Malaysia

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Cited By

Moriyani MAsaye LLe CLe TLe T(2024)Natural Language Processing for Infrastructure Resilience to Natural Disasters: A Scientometric ReviewProceedings of the 7th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2024, 4-5 April, Ho Chi Minh City, Vietnam10.1007/978-981-97-1972-3_165(1506-1513)Online publication date: 1-Jun-2024
https://doi.org/10.1007/978-981-97-1972-3_165
Thakur N(2022)Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research QuestionsAnalytics10.3390/analytics10200071:2(72-97)Online publication date: 23-Sep-2022
https://doi.org/10.3390/analytics1020007
Thakur N(undefined)Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research QuestionsSSRN Electronic Journal10.2139/ssrn.4170991
https://doi.org/10.2139/ssrn.4170991

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