Digital Twin–Driven Urban Flood Prediction and Mitigation Using Multi-Modal Environmental Data
Type: article
Abstract: Urban flooding is becoming increasingly severe due to rapid urbanization, inadequate drainage systems, and climate-induced extreme rainfall events. Conventional hydrological models often struggle to provide accurate real-time flood predictions because they rely on limited environmental data sources and static parameter assumptions. To address these challenges, this study proposes a Digital Twin–driven urban flood prediction and mitigation framework that integrates multi-modal environmental data, including IoT-based hydrological sensors, meteorological observations, LiDAR-derived elevation models, remote sensing imagery, and detailed urban drainage network information. The developed Digital Twin architecture comprises three interconnected layers: a real-time physical system layer for continuous data acquisition, a data fusion and analytics layer for hydrological and spatial feature processing, and a simulation–prediction layer for dynamic flood forecasting and mitigation planning.A hybrid deep learning model combining Long Short-Term Memory (LSTM) networks for temporal prediction and Convolutional Neural Networks (CNNs) for spatial feature extraction was deployed within the Digital Twin environment. The system was evaluated using historical rainfall and inundation records from a highly flood-prone metropolitan region. Experimental results demonstrate that the proposed framework achieves 93.7% prediction accuracy, reduces emergency-response latency by 41%, and improves flood-risk classification precision by 32% compared to baseline hydrological approaches. Furthermore, the Digital Twin enabled adaptive mitigation strategies, including optimized pump scheduling, drainage rerouting, and early-warning alert generation.Overall, the findings confirm that integrating multi-modal data within a Digital Twin architecture significantly enhances the accuracy, responsiveness, and operational capability of urban flood management systems, supporting climate-resilient and intelligent city planning.
Author: Shaik Sadulla
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7219988867