High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data

TL;DR

This paper introduces a machine learning pipeline that generates synthetic flood data to create high-resolution flood probability maps, addressing data scarcity and computational challenges in flood risk assessment.

Contribution

It presents a novel generative pipeline combining a cell-wise depth estimator and CTGAN to produce realistic synthetic inundation data for flood mapping.

Findings

Synthetic flood data closely matches real data in distribution and correlation.

The pipeline enables scalable generation of flood maps with high spatial resolution.

Validation confirms the accuracy and effectiveness of the synthetic data for flood risk analysis.

Abstract

High-resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant computation and time effort, which inhibit its feasibility. To address this gap, this study introduces Precipitation-Flood Depth Generative Pipeline, a novel methodology that leverages generative machine learning to generate large-scale synthetic inundation data to produce probabilistic flood maps. With a focus on Harris County, Texas, Precipitation-Flood Depth Generative Pipeline begins with training a cell-wise depth estimator using a number of precipitation-flood events model with a physics-based model. This cell-wise depth estimator, which emphasizes precipitation-based features, outperforms universal models. Subsequently, the Conditional Generative Adversarial Network (CTGAN) is used to conditionally generate synthetic precipitation point cloud, which are filtered using strategic thresholds to align with realistic precipitation patterns. Hence, a precipitation feature pool is constructed for each cell, enabling strategic sampling and the generation of synthetic precipitation events. After generating 10,000 synthetic events, flood probability maps are created for various inundation depths. Validation using similarity and correlation metrics confirms the accuracy of the synthetic depth distributions. The Precipitation-Flood Depth Generative Pipeline provides a scalable solution to generate synthetic flood depth data needed for high-resolution flood probability maps, which can enhance flood mitigation planning.