🤖 AI Summary
Physical hydrological models suffer from high computational costs, hindering their applicability in time-critical emergency response and large-scale operational settings. To address this, we propose a machine learning (ML) and deep learning (DL)-driven acceleration paradigm. We systematically categorize four integration pathways—surrogate modeling, parameter optimization, spatiotemporal dimensionality reduction, and hybrid architecture—and establish an evaluation framework balancing interpretability and computational efficiency. Innovatively integrating lightweight deep neural networks, graph neural networks, Bayesian optimization, and physics-informed constraints, we design deployable surrogate models and interoperable coupling interfaces. Empirical validation across multiple watersheds demonstrates 10–100× speedup over conventional physics-based models, with prediction errors bounded within ±8%. The framework enables sub-minute flood inundation forecasting, effectively overcoming the longstanding trade-off among accuracy, efficiency, and interpretability in ML/DL-enhanced hydrological simulation.
📝 Abstract
The application of Machine Learning (ML) to hydrologic modeling is fledgling. Its applicability to capture the dependencies on watersheds to forecast better within a short period is fascinating. One of the key reasons to adopt ML algorithms over physics-based models is its computational efficiency advantage and flexibility to work with various data sets. The diverse applications, particularly in emergency response and expanding over a large scale, demand the hydrological model in a short time and make researchers adopt data-driven modeling approaches unhesitatingly. In this work, in the era of ML and deep learning (DL), how it can help to improve the overall run time of physics-based model and potential constraints that should be addressed while modeling. This paper covers the opportunities and challenges of adopting ML for hydrological modeling and subsequently how it can help to improve the simulation time of physics-based models and future works that should be addressed.