Machine Learning for Flood Prediction in Indonesia: Providing Online Access for Disaster Management Control
Reta L. Puspasari1, Daeung Yoon2, Hyun Kim3, Kyoung-Woong Kim1,*
1School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Korea
2Department of Energy and Resources Engineering Chonnam National University, Korea
3Division of Environmental Health Sciences, School of Public Health, University of Minnesota, United States
2Department of Energy and Resources Engineering Chonnam National University, Korea
3Division of Environmental Health Sciences, School of Public Health, University of Minnesota, United States
Received February 6, 2023; Revised February 22, 2023; Accepted February 23, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract- As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.
- Keywords : machine learning algorithm, random forest, online access website, flood mitigation, F-score
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