[HTML][HTML] A brief review of random forests for water scientists and practitioners and their recent history in water resources

H Tyralis, G Papacharalampous, A Langousis - Water, 2019 - mdpi.com
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

Machine learning for hydrologic sciences: An introductory overview

T Xu, F Liang - Wiley Interdisciplinary Reviews: Water, 2021 - Wiley Online Library
The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

Parameter estimation and uncertainty analysis in hydrological modeling

PA Herrera, MA Marazuela… - Wiley Interdisciplinary …, 2022 - Wiley Online Library
Nowadays, mathematical models of hydrological systems are used routinely to guide
decision making in diverse subjects, such as: environmental and risk assessments, design …

[HTML][HTML] Groundwater sustainability: A review of the interactions between science and policy

AS Elshall, AD Arik, AI El-Kadi, S Pierce… - Environmental …, 2020 - iopscience.iop.org
Concerns over groundwater depletion and ecosystem degradation have led to the
incorporation of the concept of groundwater sustainability as a groundwater policy …

Ensemble modelling framework for groundwater level prediction in urban areas of India

B Yadav, PK Gupta, N Patidar, SK Himanshu - Science of the Total …, 2020 - Elsevier
India is facing the worst water crisis in its history and major Indian cities which accommodate
about 50% of its population will be among highly groundwater stressed cities by 2020. In …

An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions

H Chu, W Wu, QJ Wang, R Nathan, J Wei - Environmental Modelling & …, 2020 - Elsevier
Hydrodynamic models are commonly used to understand flood risk and inform flood
management decisions. However, their high computational cost can impose practical limits …

Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest

JM Sadler, JL Goodall, MM Morsy, K Spencer - Journal of hydrology, 2018 - Elsevier
Sea level rise has already caused more frequent and severe coastal flooding and this trend
will likely continue. Flood prediction is an essential part of a coastal city's capacity to adapt to …

Groundwater contamination source identification and high-dimensional parameter inversion using residual dense convolutional neural network

X Xia, S Jiang, N Zhou, J Cui, X Li - Journal of Hydrology, 2023 - Elsevier
Data assimilation for high-dimensional parameter joint inversion of multiple time-varying
source strength and hydraulic conductivity fields can be computationally intensive as a large …

Estimation of flexible pavement structural capacity using machine learning techniques

N Karballaeezadeh, H Ghasemzadeh Tehrani… - Frontiers of Structural …, 2020 - Springer
The most common index for representing structural condition of the pavement is the
structural number. The current procedure for determining structural numbers involves …

An efficient Bayesian inversion method for seepage parameters using a data-driven error model and an ensemble of surrogates considering the interactions between …

H Yu, X Wang, B Ren, T Zeng, M Lv, C Wang - Journal of Hydrology, 2022 - Elsevier
The Bayesian method has been increasingly applied to the inversion of seepage
parameters owing to its superiority of considering the uncertainty in the inversion process …