---
title: From Meteorology to Market: A Geo-Hierarchical Deep Learning Approach for Flood Risk Pricing
url: https://www.ml-quant.com/papers/ssrn/4692475/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4692475
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4692475
featured: 2024-01-17
citations: 0
topic: ML & AI Methods
---


# From Meteorology to Market: A Geo-Hierarchical Deep Learning Approach for Flood Risk Pricing

Geo-Hierarchical Deep Learning: A new deep learning framework enhances flood risk modeling, providing more accurate pricing and reducing capital requirements, as shown in a Mississippi River case study.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4692475
- Identifier: SSRN 4692475
- Released: 2022-01-13
- First featured: Quant Letter No. 33 (2024-01-17): https://www.ml-quant.com/issues/2024-01-17/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: ML & AI Methods

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