Differentiable modelling to unify machine learning and physical models for geosciences
Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …
Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling: Review of current applications and trends
This paper reviews the current GeoAI and machine learning applications in hydrological and
hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial …
hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial …
Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder
MS Jahangir, J Quilty - Journal of Hydrology, 2024 - Elsevier
Probabilistic hydrological forecasting has gained increasing importance in recent years, as it
offers essential information for risk-based decision-making and flood management …
offers essential information for risk-based decision-making and flood management …
Airbnb price prediction using machine learning and sentiment analysis
P Rezazadeh Kalehbasti, L Nikolenko… - international cross-domain …, 2021 - Springer
Pricing a property and evaluating the proposed price for a property are challenges that,
respectively, owners and customers of Airbnb rentals face on a daily basis. This paper aims …
respectively, owners and customers of Airbnb rentals face on a daily basis. This paper aims …
Three-dimensional numerical modeling of local scour: A state-of-the-art review and perspective
This article aims to provide a comprehensive appraisal on the current status of three-
dimensional (3D) numerical modeling of local scour around instream hydraulic structures …
dimensional (3D) numerical modeling of local scour around instream hydraulic structures …
Bathymetry inversion using a deep‐learning‐based surrogate for shallow water equations solvers
River bathymetry is critical for many aspects of water resources management. We propose
and demonstrate a bathymetry inversion method using a deep‐learning‐based surrogate for …
and demonstrate a bathymetry inversion method using a deep‐learning‐based surrogate for …
Using deep learning to model the groundwater tracer radon in coastal waters
Submarine groundwater discharge (SGD) is an important driver of coastal biogeochemical
budgets worldwide. Radon (222Rn) has been widely used as a natural geochemical tracer …
budgets worldwide. Radon (222Rn) has been widely used as a natural geochemical tracer …
Bed topography inference from velocity field using deep learning
M Kiani-Oshtorjani, C Ancey - Water, 2023 - mdpi.com
Measuring bathymetry has always been a major scientific and technological challenge. In
this work, we used a deep learning technique for inferring bathymetry from the depth …
this work, we used a deep learning technique for inferring bathymetry from the depth …
A robust filtering algorithm based on the estimation of tracer visibility and stability for large scale particle image velocimetry
L Li, H Yan - Flow Measurement and Instrumentation, 2022 - Elsevier
Image velocimetry for open channel is safe, efficient and environmentally friendly and large-
scale particle image velocimetry (LSPIV) is one of the most adopted methods. Furthermore …
scale particle image velocimetry (LSPIV) is one of the most adopted methods. Furthermore …
Finding optimal strategies for river quality assessment using machine learning and deep learning models
The accurate evaluation of river quality assessment is essential for human health,
ecosystem functionality, economic growth, and future population growth. In most cases, river …
ecosystem functionality, economic growth, and future population growth. In most cases, river …