XAI for Earth Sciences

Last updated Sep 29, 2022 Edit Source

See:

• AI/XAI
• AI4ES/Causal modeling in ES

• #PAPER Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning (McGovern et al. 2019)
• #PAPER Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability (Toms 2020)
  • Showed that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data
  • Used two methods for neural network interpretation: backwards optimization and LRP. Both project the decision pathways of a network back onto the original input dimensions
• #PAPER Indicator patterns of forced change learned by an artificial neural network (Barnes 2020)
• #PAPER Evaluation, Tuning and Interpretation of Neural Networks for Meteorological Applications (Ebert-Uphoff 2020)
• #PAPER Detecting climate signals using explainable AI with single-forcing large ensembles (Labe 2021)
• #PAPER Using Machine Learning to Analyze Physical Causes of Climate Change: A Case Study of U.S. Midwest Extreme Precipitation (Davenport 2021)
  • use a neural network to predict extreme precipitation from daily sea level pressure and 500-hPa geopotential height fields
  • CNN 2 layers to predict extreme precipitation (p95) based on the average precipitation over a rectangular region covering the Upper Mississippi Watershed and the eastern portion of the Missouri Watershed
  • use daily mean sea level pressure (SLP) and 500-hPa geopotential height (GPH) anomalies calculated from the NCEP/NCAR-R1 reanalysis, 2.5 deg resolution on a larger spatial domain that covers the continental U.S. and surrounding oceans
• #PAPER Oceanic Harbingers of Pacific Decadal Oscillation Predictability in CESM2 Detected by Neural Networks (Gordon 2021)
• #POSTER Mapped-PCMCI: an algorithm for causal discovery at the grid level (Tibau Alberdi 2021)
• #PAPER Assessing Decadal Predictability in an Earth-System Model Using Explainable Neural Networks (Toms 2021)
• #PAPER Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks (Labe 2022)
• #PAPER Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience (Mamalakis 2022)
• #PAPER Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset (Mamalakis 2022)
  • #CODE https://github.com/amamalak/Neural-Network-Attribution-Benchmark-for-Regression