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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2208.07899 |
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| _version_ | 1866916790798385152 |
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| author | Dietz, Lindsey Arya, Sakshi Subedi, Vishal Ganguly, Auroop R. Chatterjee, Snigdhansu |
| author_facet | Dietz, Lindsey Arya, Sakshi Subedi, Vishal Ganguly, Auroop R. Chatterjee, Snigdhansu |
| contents | Bayesian hierarchical models are proposed for modeling tropical cyclone characteristics and their damage potential in the Atlantic basin. We model the joint probability distribution of tropical cyclone characteristics and their damage potential at two different temporal scales, while taking several climate indices into account. First, a predictive model for an entire season is developed that forecasts the number of cyclone events that will take place, the probability of each cyclone causing some amount of damage, and the monetized value of damages. Then, specific characteristics of individual cyclones are considered to predict the monetized value of the damage it will cause. Robustness studies are conducted and excellent prediction power is demonstrated across different data science models and evaluation techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_07899 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Predictions of damages from Atlantic tropical cyclones: a hierarchical Bayesian study on extremes Dietz, Lindsey Arya, Sakshi Subedi, Vishal Ganguly, Auroop R. Chatterjee, Snigdhansu Applications 62P12 Bayesian hierarchical models are proposed for modeling tropical cyclone characteristics and their damage potential in the Atlantic basin. We model the joint probability distribution of tropical cyclone characteristics and their damage potential at two different temporal scales, while taking several climate indices into account. First, a predictive model for an entire season is developed that forecasts the number of cyclone events that will take place, the probability of each cyclone causing some amount of damage, and the monetized value of damages. Then, specific characteristics of individual cyclones are considered to predict the monetized value of the damage it will cause. Robustness studies are conducted and excellent prediction power is demonstrated across different data science models and evaluation techniques. |
| title | Predictions of damages from Atlantic tropical cyclones: a hierarchical Bayesian study on extremes |
| topic | Applications 62P12 |
| url | https://arxiv.org/abs/2208.07899 |