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Editorial: Data-driven machine learning for advancing hydrological and hydraulic predictability

Journal Article · · Frontiers in Water
 [1];  [2];  [3]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  2. Univ. of Oklahoma, Norman, OK (United States)
  3. Pennsylvania State Univ., University Park, PA (United States)
+ Show Author Affiliations
The growing influence of machine learning (ML) in every aspect of our lives has led to revolutionary advancements in our understanding, prediction, and decision-making capabilities. One field that stands to benefit greatly from applying these techniques includes hydrology and hydraulics. The ability to predict hydrological and hydraulic phenomena with greater accuracy and reliability is of utmost importance, given the increasing threats posed by climate change and extreme weather/climate events. In this editorial, we explore the significant contributions made by four recent studies that aim to advance hydrological and hydraulic predictability through data-driven ML.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1997649
Journal Information:
Frontiers in Water, Journal Name: Frontiers in Water Journal Issue: 5 Vol. 5; ISSN 2624-9375
Publisher:
Frontiers Media S.A.Copyright Statement
Country of Publication:
United States
Language:
English

References (5)

Rapid Prediction Model for Urban Floods Based on a Light Gradient Boosting Machine Approach and Hydrological–Hydraulic Model journal February 2023
A novel paradigm for integrating physics-based numerical and machine learning models: A case study of eco-hydrological model journal May 2023
Two-dimensional convolutional neural network outperforms other machine learning architectures for water depth surrogate modeling journal January 2023
A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling journal February 2023
Hybrid forecasting: blending climate predictions with AI models journal May 2023

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Related Subjects

42 ENGINEERING
climate change
data-driven machine learning
explainable machine learning
hydrological and hydraulic predictability
uncertainty quantification