Deep learning for geophysics: Current and future trends
Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …
approaches, has attracted increasing attention in geophysical community, resulting in many …
A comprehensive review of deep learning applications in hydrology and water resources
The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume,
variety and velocity of water-related data are increasing due to large-scale sensor networks …
variety and velocity of water-related data are increasing due to large-scale sensor networks …
Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets
Abstract The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement
(IMERG) produces the latest generation of satellite precipitation estimates and has been …
(IMERG) produces the latest generation of satellite precipitation estimates and has been …
XGBoost-based method for flash flood risk assessment
M Ma, G Zhao, B He, Q Li, H Dong, S Wang, Z Wang - Journal of Hydrology, 2021 - Elsevier
Flash flood risk assessment, a widely applied technology in preventing catastrophic flash
flood disasters, has become the current research hotspot. However, most existing machine …
flood disasters, has become the current research hotspot. However, most existing machine …
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …
of environment and water management (EWM). Big Data are information assets …
Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins
Abstract Launched in May 2018, the Gravity Recovery and Climate Experiment Follow‐On
mission (GRACE‐FO)—the successor of the erstwhile GRACE mission—monitors changes …
mission (GRACE‐FO)—the successor of the erstwhile GRACE mission—monitors changes …
Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling
S Razavi - Environmental Modelling & Software, 2021 - Elsevier
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …
have created tremendous excitement and opportunities in the earth and environmental …
[KNIHA][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China
To improve the accuracy of quantitative precipitation estimation (QPE), numerous models
have been developed for merging satellite and gauge precipitation. However, most …
have been developed for merging satellite and gauge precipitation. However, most …
Satellite remote sensing of precipitation and the terrestrial water cycle in a changing climate
V Levizzani, E Cattani - Remote sensing, 2019 - mdpi.com
The water cycle is the most essential supporting physical mechanism ensuring the existence
of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is …
of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is …