Computational methodologies for critical infrastructure resilience modeling: A review

A Ji, R He, W Chen, L Zhang - Advanced Engineering Informatics, 2024 - Elsevier
Modeling the resilience of critical infrastructures (CIs) is broadly viewed as critical to
maintaining the normal condition of CIs as a result of frequent threats that can disrupt safety …

Physics-aware machine learning revolutionizes scientific paradigm for machine learning and process-based hydrology

Q Xu, Y Shi, J Bamber, Y Tuo, R Ludwig… - arXiv preprint arXiv …, 2023 - arxiv.org
Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …

Risk prediction based on oversampling technology and ensemble model optimized by tree-structured parzed estimator

H Wang, X Guan, Y Meng, H Wang, H Xu, Y Liu… - International Journal of …, 2024 - Elsevier
High accuracy prediction of urban flood risk is conducive to avoid potential losses, however,
it's negatively affected by unbalanced data. Furthermore, ensemble model has been …

Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations

AM Tartakovsky, Y Zong - Journal of Computational Physics, 2024 - Elsevier
We propose a physics-informed machine-learning method based on space-time-dependent
Karhunen-Loève expansions (KLEs) of the state variables and the residual least-square …

[HTML][HTML] Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models

DF Muñoz, H Moftakhari… - Hydrology and Earth …, 2024 - hess.copernicus.org
Compound flood (CF) modeling enables the simulation of nonlinear water level dynamics in
which concurrent or successive flood drivers synergize, producing larger impacts than those …

Uncertainty analysis of simplified 1D and 2D shallow water equations via the Karhunen–Loéve expansion and Monte Carlo simulations

SH Malekhosseini, H Khorshidi… - … Research and Risk …, 2024 - Springer
The stochastic solution of wave propagation through simplified shallow water equations,
described by a system of 1D and 2D linear equations, has been investigated by considering …

Geographic heterogeneity of activation functions in urban real-time flood forecasting: Based on seasonal trend decomposition using Loess-Temporal Convolutional …

S Huan - Journal of Hydrology, 2024 - Elsevier
Urban real-time flood forecasting is crucial for flood prevention and sustainable
development, but it poses challenges due to data inputs and activation functions selection in …

Uncertainty analysis for design rainfall estimation using peaks-over-threshold model and specially formulated pivotal quantities

W Zheng, S Liu, Z Zhou, Y Guo - Journal of Hydrology, 2025 - Elsevier
Uncertainties associated with the estimated design rainfall depths are difficult to quantify,
especially if the uncertainties of the threshold used in the traditional peaks-over-threshold …

[HTML][HTML] Establishing correlations between time series of wastewater parameters under extreme and regular weather conditions

M Cheng, M Evangelisti, S Gobeyn, F Avolio… - Journal of …, 2025 - Elsevier
This study investigates the correlations between key wastewater parameters–water level,
turbidity, and electrical conductivity–under varying weather conditions, including extreme …

[PDF][PDF] Blending Physics with Data Using An Efficient Gaussian Process Regression with Soft Inequality and Monotonicity Constraints

D Kochan, X Yang - Frontiers in Mechanical Engineering - engineering.lehigh.edu
In this work, we propose a new Gaussian process (GP) regression framework that enforces
the 33 physical constraints in a probabilistic manner. Specifically, we focus on inequality and …