Sustainability 2026, 18(15), 7820; https://doi.org/10.3390/su18157820 (registering DOI) - 2 Aug 2026
Abstract
Extreme climate events and rapid urbanization pose severe threats to urban water sustainability and environmental resilience. This study presents a surrogate-assisted hydrological simulation framework designed to support sustainable urban drainage planning and multi-objective scenario exploration. The proposed approach integrates a physics-based hydrodynamic model
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Extreme climate events and rapid urbanization pose severe threats to urban water sustainability and environmental resilience. This study presents a surrogate-assisted hydrological simulation framework designed to support sustainable urban drainage planning and multi-objective scenario exploration. The proposed approach integrates a physics-based hydrodynamic model with a machine learning surrogate (XGBoost) to emulate system responses across diverse design configurations. Under the assumed setup, the framework rapidly evaluates hydrological indicators (peak runoff, flood depth, duration, and extent) alongside microclimatic co-benefits (urban cooling and ventilation) driven by blue-green infrastructure. The surrogate model demonstrates high fidelity
(This article belongs to the Section Green Building)
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