Artificial intelligence for geoscience: Progress, challenges and perspectives
This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …
traditional physics-based models to modern data-driven approaches facilitated by significant …
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
Machine learning in weather prediction and climate analyses—applications and perspectives
B Bochenek, Z Ustrnul - Atmosphere, 2022 - mdpi.com
In this paper, we performed an analysis of the 500 most relevant scientific articles published
since 2018, concerning machine learning methods in the field of climate and numerical …
since 2018, concerning machine learning methods in the field of climate and numerical …
[HTML][HTML] High-resolution downscaling with interpretable deep learning: Rainfall extremes over New Zealand
The gap in resolution between existing global climate model output and that sought by
decision-makers drives an ongoing need for climate downscaling. Here we test the extent to …
decision-makers drives an ongoing need for climate downscaling. Here we test the extent to …
How interpretable machine learning can benefit process understanding in the geosciences
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …
new opportunities to improve our understanding of the complex Earth system. IML goes …
A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning
Climate change (CC) is one of the greatest threats to human health, safety, and the
environment. Given its current and future impacts, numerous studies have employed …
environment. Given its current and future impacts, numerous studies have employed …
Machine learning–based extreme event attribution
The observed increase in extreme weather has prompted recent methodological advances
in extreme event attribution. We propose a machine learning–based approach that uses …
in extreme event attribution. We propose a machine learning–based approach that uses …
Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm
B Zhang, Y Zhang, X Jiang - Scientific Reports, 2022 - nature.com
Ozone is one of the most important air pollutants, with significant impacts on human health,
regional air quality and ecosystems. In this study, we use geographic information and …
regional air quality and ecosystems. In this study, we use geographic information and …
Development of machine learning flood model using artificial neural network (ann) at var river
Data-driven flow forecasting models, such as Artificial Neural Networks (ANNs), are
increasingly used for operational flood warning systems. In this research, we systematically …
increasingly used for operational flood warning systems. In this research, we systematically …
Machine learning in absorption-based post-combustion carbon capture systems: A state-of-the-art review
The enormous consumption of fossil fuels from various human activities leads to a significant
amount of anthropogenic CO 2 emission into the atmosphere, which has already massively …
amount of anthropogenic CO 2 emission into the atmosphere, which has already massively …