Dataset and codes to analyze the effects of FAR and MER on flood damage: case study of the 2018 Japan Floods
The dataset and R codes to investigate the effects of the false alarm ratio (FAR) and missed event ratio (MER) on flood damage by applying Bayesian regression analyses to open data on the 2018 Japan Floods in 127 municipalities in four prefectures (i.e., Okayama, Hiroshima, Ehime, and Fukuoka).
The data for FAR and MER were collected from the real-time flood warning map (Kouzui Kikikuru in Japanese) during the 2018 Japan Floods, which provides limited open data on warning performance.
The data for disaster damage, namely (1) fatalities, (2) injuries, (3) economic losses to general assets, and (4) economic losses to crops during the 2018 Japan Floods, were collected from technical disaster damage reports compiled by the prefectures and the Cabinet Office.
Additional detailed instructions are provided in a readme file.
History
Corresponding author email address
kotani.h.15c7@m.isct.ac.jpCopyright
© The Author(s) 2025Common Metadata Elements (Only for the items supported by Japanese public funds)
- This item includes dataset(s) related to publicly funded research (fill in all the fields below)
Funder
JSPS; JSTProgram Name
科学研究費助成事業; ムーンショット型研究開発事業Japan Grant Number
JP22K18822; JPMJMS2281Project Name
水害予測におけるオオカミ少年効果の理論・実証分析：新しい水害警報の設計への挑戦; 社会的意思決定を支援する気象－社会結合系の制御理論Data No.
123Research Field
- Social Infrastructure （社会基盤）
Access rights
- Open（公開）
