An Interpretable Model of Climate Change Using Correlative Learning

This paper presents an interpretable model of climate change using correlative learning. To address the challenge in determining changes in global temperature and precipitation that may indicate climate change, the authors trained a two-hidden-layer neural network on global temperatures and precipitations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to predict the year. A new way of interpreting what the neural network has learned is explored by using Alopex, a stochastic correlative learning algorithm, to find optimal temperature and precipitation maps that best predict a given year. These maps are compared over multiple years to show how temperature and precipitation patterns indicative of each year change over time. The authors demonstrate their approach by applying it to CMIP6 data and comparing their results with previous studies using linear regression models on CMIP5 data. They also provide a detailed explanation of Alopex as a global method for interpreting what a neural network has learned and demonstrate its effectiveness by using it to interpret what a neural network has learned when trained on MNIST data. This paper provides valuable insights into how machine learning can be used to understand climate change indicators and offers an interpretable model that can help domain experts better understand how temperatures and precipitations change over time, ultimately aiding efforts to tackle climate change.

Interpretable Model of Climate Change Using Correlative Learning

Climate change is one of the most pressing issues facing humanity today. To better understand how global temperatures and precipitation patterns are changing, researchers have developed various models to predict future climate trends. However, many of these models lack interpretability, making it difficult for domain experts to understand what the model has learned.

In this paper, the authors present an interpretable model of climate change using correlative learning. The authors trained a two-hidden-layer neural network on global temperatures and precipitations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to predict a given year. To interpret what the neural network has learned, they used Alopex, a stochastic correlative learning algorithm that finds optimal temperature and precipitation maps that best predict a given year.

The authors demonstrate their approach by applying it to CMIP6 data and comparing their results with previous studies using linear regression models on CMIP5 data.