A transdisciplinary review of deep learning research and its relevance for water resources scientists
C Shen - Water Resources Research, 2018 - Wiley Online Library
Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …
industries, daily lives, and various scientific disciplines in recent years. DL represents …
Machine learning for hydrologic sciences: An introductory overview
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 …
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …
[图书][B] Random fields for spatial data modeling
DT Hristopulos - 2020 - Springer
The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …
and robust GIScience methods for use in transdisciplinary problem solving and decision …
An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables
A new stochastic weather generator, Advanced WEather GENerator for a two‐dimensional
grid (AWE‐GEN‐2d) is presented. The model combines physical and stochastic approaches …
grid (AWE‐GEN‐2d) is presented. The model combines physical and stochastic approaches …
Stochastic weather generators: an overview of weather type models
A recurrent issue encountered in environmental, ecological or agricultural impact studies in
which climate is an important driving force is to provide fast and realistic simulations of …
which climate is an important driving force is to provide fast and realistic simulations of …
Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency
SM Papalexiou - Advances in water resources, 2018 - Elsevier
Hydroclimatic processes come in all “shapes and sizes”. They are characterized by different
spatiotemporal correlation structures and probability distributions that can be continuous …
spatiotemporal correlation structures and probability distributions that can be continuous …
River/stream water temperature forecasting using artificial intelligence models: a systematic review
S Zhu, AP Piotrowski - Acta Geophysica, 2020 - Springer
Water temperature is one of the most important indicators of aquatic system, and accurate
forecasting of water temperature is crucial for rivers. It is a complex process to accurately …
forecasting of water temperature is crucial for rivers. It is a complex process to accurately …
Intercomparison of statistical and dynamical downscaling models under the EURO-and MED-CORDEX initiative framework: present climate evaluations
P Vaittinada Ayar, M Vrac, S Bastin, J Carreau… - Climate dynamics, 2016 - Springer
Given the coarse spatial resolution of General Circulation Models, finer scale projections of
variables affected by local-scale processes such as precipitation are often needed to drive …
variables affected by local-scale processes such as precipitation are often needed to drive …
A stochastic model for high‐resolution space‐time precipitation simulation
High‐resolution space‐time stochastic models for precipitation are crucial for hydrological
applications related to flood risk and water resources management. In this study, we present …
applications related to flood risk and water resources management. In this study, we present …
Review of dependence modeling in hydrology and water resources
Various methods have been developed over the past five decades for dependence
modeling of multivariate variables in hydrology and water resources, but there has been no …
modeling of multivariate variables in hydrology and water resources, but there has been no …