Machine Learning for Flood Prediction in Indonesia: Providing Online Access for Disaster Management Control

• 영문명: Machine Learning for Flood Prediction in Indonesia: Providing Online Access for Disaster Management Control
• 발행기관: 대한자원환경지질학회
• 저자명: Reta L. Puspasari 윤대웅 Hyun Kim 김경웅
• 간행물 정보: 『자원환경지질』56권 1호, 65~73쪽, 전체 9쪽
• 주제분류: 자연과학 > 지질학
• 파일형태: PDF
• 발행일자: 2023.02.28

영문 초록

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.

목차

1. Introduction

2. Materials and Methods

3. Results and Discussions

4. Conclusions

Acknowledgments

References

키워드

machine learning algorithm random forest online access website flood mitigation F-score