Random forest-based multi-hazard loss estimation using hypothetical data at seismic and tsunami monitoring networks
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作者:
Li, Yao
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Western Univ, Dept Stat & Actuarial Sci, London, ON, CanadaWestern Univ, Dept Stat & Actuarial Sci, London, ON, Canada
Li, Yao
[1
]
Goda, Katsuichiro
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Western Univ, Dept Stat & Actuarial Sci, London, ON, Canada
Western Univ, Dept Earth Sci, London, ON, CanadaWestern Univ, Dept Stat & Actuarial Sci, London, ON, Canada
Goda, Katsuichiro
[1
,2
]
机构:
[1] Western Univ, Dept Stat & Actuarial Sci, London, ON, Canada
[2] Western Univ, Dept Earth Sci, London, ON, Canada
This article presents a novel approach to estimate multi-hazard loss in a post-event situation, resulting from cascading earthquake and tsunami events with machine learning for the first time. The proposed methodology combines the power of random forest (RF) with data that are simulated at seismic and tsunami monitoring locations. The RF model is well-suited for predicting highly nonlinear multi-hazard loss because of its nonparametric regression and ensemble learning capabilities. The study targets the cities of Iwanuma and Onagawa in Tohoku, Japan, where seismic and tsunami monitoring networks have been deployed. To encompass a diverse range of future multi-hazard loss estimation, an RF model is constructed based on 4000 simulated earthquake events with peak ground velocity and tsunami wave amplitude captured at ground-motion monitoring sites and offshore wave monitoring sensors, respectively. The incorporation of 10 ground-motion monitoring sites and five offshore wave monitoring sensors significantly enhances the model's forecasting power, leading to a notable 60% decrease in mean squared error and 20% increase in the R 2 value compared to scenarios where no monitoring sensors are utilized. By harnessing the capabilities of RF and leveraging detailed sensing data, RF achieves R 2 values over 90%, which can contribute to enhanced disaster risk management.
机构:
East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China
Univ Queensland, Sch Earth & Environm Sci, Brisbane, Qld 4072, AustraliaEast China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
Zhao, Xizhi
Yu, Bailang
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East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R ChinaEast China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
Yu, Bailang
Liu, Yan
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Univ Queensland, Sch Earth & Environm Sci, Brisbane, Qld 4072, AustraliaEast China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
Liu, Yan
Chen, Zuoqi
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East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R ChinaEast China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
Chen, Zuoqi
Li, Qiaoxuan
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East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R ChinaEast China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
Li, Qiaoxuan
Wang, Congxiao
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East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R ChinaEast China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
Wang, Congxiao
Wu, Jianping
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East China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China
East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R ChinaEast China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China