Modelling the behaviour of corporations during the flood damage recovery process using multi‐agent deep reinforcement learning-Reference-Cited by-同舟云学术

Modelling the behaviour of corporations during the flood damage recovery process using multi‐agent deep reinforcement learning

Published:2022-08-06 Issue:4 Volume:15 Page:
ISSN:1753-318X
Container-title:Journal of Flood Risk Management
language:en
Short-container-title:J Flood Risk Management

Author:

Yang Shaofeng¹^ORCID,Ogawa Yoshiki²^ORCID,Ikeuchi Koji³,Shibasaki Ryosuke⁴,Okuma Yuuki⁵

Affiliation:

1. Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan

2. Institute of Industrial Science The University of Tokyo Tokyo Japan

3. Graduate School of Engineering The University of Tokyo Tokyo Japan

4. Center of Spatial Information Science The University of Tokyo Tokyo Japan

5. Mitsubishi Research Institute, Inc. Tokyo Japan

Funder

Japan Society for the Promotion of Science

Publisher

Wiley

Subject

Water Science and Technology,Safety, Risk, Reliability and Quality,Geography, Planning and Development,Environmental Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jfr3.12845

Reference44 articles.

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2. Aon plc. (2018).Weather climate & catastrophe insight 2017 annual report.https://reliefweb.int/sites/reliefweb.int/files/resources/20180124-ab-if-annual-report-weather-climate-2017.pdf

3. Comparing the Economic Impact of Natural Disasters Generated by Different Input–Output Models: An Application to the 2007 Chehalis River Flood (WA)

4. Assessing direct and indirect economic impacts of a flood event through the integration of spatial and computable general equilibrium modelling

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