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Found 14 papers, 3 papers with code
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks
Then we provide an overview of many different concepts and techniques that are helpful for the interpretation of meteorological image sequences, such as (1) feature engineering methods to strengthen the desired signal in the input, using meteorological knowledge, classic image processing, harmonic analysis and topological data analysis (2) explain how different convolution filters (2D/3D/LSTM-convolution) can be utilized strategically in convolutional neural network architectures to find patterns in both space and time (3) discuss the powerful new concept of 'attention' in neural networks and the powerful abilities it brings to the interpretation of image sequences (4) briefly survey strategies from unsupervised, self-supervised and transfer learning to reduce the need for large labeled datasets.
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience
We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly.
A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science
One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find, but why those results are to be expected given what we know about the theory of persistent homology.
Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?
We provide a general guide to using SELFs, including technical challenges and the final Python code, as well as demonstrating their use for the convection problem.
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiotemporal patterns.
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences
In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system.
CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences -- Version 1
Standard loss functions do not cover all the needs of the environmental sciences, which makes it important for scientists to be able to develop their own custom loss functions so that they can implement many of the classic performance measures already developed in environmental science, including measures developed for spatial model verification.
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset
Here, we provide a framework, based on the use of additively separable functions, to generate attribution benchmark datasets for regression problems for which the ground truth of the attribution is known a priori.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications
Neural networks have opened up many new opportunities to utilize remotely sensed images in meteorology.
Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations
Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques.
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason.
Machine Learning for the Geosciences: Challenges and Opportunities
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet.
High-Dimensional Dependency Structure Learning for Physical Processes
We compare ACLIME-ADMM with baselines on both synthetic data simulated by partial differential equations (PDEs) that model advection-diffusion processes, and real data (50 years) of daily global geopotential heights to study information flow in the atmosphere.
Using Causal Discovery to Track Information Flow in Spatio-Temporal Data - A Testbed and Experimental Results Using Advection-Diffusion Simulations
Causal discovery algorithms based on probabilistic graphical models have emerged in geoscience applications for the identification and visualization of dynamical processes.
