Methods to improve run time of hydrologic models: opportunities and challenges in the machine learning era

TL;DR

This paper explores how machine learning and deep learning can enhance the computational efficiency of hydrological models, discussing opportunities, challenges, and future directions for integrating ML with physics-based approaches.

Contribution

It provides a comprehensive overview of opportunities and challenges in applying ML to improve hydrological model run times and discusses future research directions.

Findings

ML can significantly reduce hydrological model run times.

Adopting ML introduces challenges like data quality and model interpretability.

Future work should focus on integrating ML with physics-based models effectively.

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.