Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0

• URL: http://arxiv.org/abs/2308.11854v1
• Date: Wed, 23 Aug 2023 01:08:01 GMT
• Title: Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0
• Authors: Anmol Chaure, Ashok Kumar Behera, Sudip Bhattacharya
• Abstract summary: ClimateBench is a benchmarking dataset for evaluating the performance of machine learning emulators designed for climate data. This study focuses on evaluating non-linear regression models using the aforementioned dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Climate projections using data driven machine learning models acting as emulators, is one of the prevailing areas of research to enable policy makers make informed decisions. Use of machine learning emulators as surrogates for computationally heavy GCM simulators reduces time and carbon footprints. In this direction, ClimateBench [1] is a recently curated benchmarking dataset for evaluating the performance of machine learning emulators designed for climate data. Recent studies have reported that despite being considered fundamental, regression models offer several advantages pertaining to climate emulations. In particular, by leveraging the kernel trick, regression models can capture complex relationships and improve their predictive capabilities. This study focuses on evaluating non-linear regression models using the aforementioned dataset. Specifically, we compare the emulation capabilities of three non-linear regression models. Among them, Gaussian Process Regressor demonstrates the best-in-class performance against standard evaluation metrics used for climate field emulation studies. However, Gaussian Process Regression suffers from being computational resource hungry in terms of space and time complexity. Alternatively, Support Vector and Kernel Ridge models also deliver competitive results and but there are certain trade-offs to be addressed. Additionally, we are actively investigating the performance of composite kernels and techniques such as variational inference to further enhance the performance of the regression models and effectively model complex non-linear patterns, including phenomena like precipitation.

Related papers

• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• The impact of internal variability on benchmarking deep learning climate emulators [2.3342885570554652]
  Full-complexity Earth system models (ESMs) are computationally very expensive, limiting their use in exploring the climate outcomes of multiple emission pathways. More efficient emulators that approximate ESMs can directly map emissions onto climate datasets. We investigate a popular benchmark in data-driven climate emulation, ClimateBench, on which deep learning-based emulators are currently achieving the best performance.
  arXiv  Detail & Related papers  (2024-08-09T18:17:17Z)
• Towards Learning Stochastic Population Models by Gradient Descent [0.0]
  We show that simultaneous estimation of parameters and structure poses major challenges for optimization procedures. We demonstrate accurate estimation of models but find that enforcing the inference of parsimonious, interpretable models drastically increases the difficulty.
  arXiv  Detail & Related papers  (2024-04-10T14:38:58Z)
• Predictive Analytics of Varieties of Potatoes [2.336821989135698]
  We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties.
  arXiv  Detail & Related papers  (2024-04-04T00:49:05Z)
• ZeroShape: Regression-based Zero-shot Shape Reconstruction [56.652766763775226]
  We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets. We show that ZeroShape achieves superior performance over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-21T01:56:34Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates [68.09049111171862]
  This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. We formulate the regression-free model updates into a constrained optimization problem. We empirically analyze how model ensemble reduces regression.
  arXiv  Detail & Related papers  (2021-05-07T03:33:00Z)
• Do We Really Need Deep Learning Models for Time Series Forecasting? [4.2698418800007865]
  Time series forecasting is a crucial task in machine learning, as it has a wide range of applications. Deep learning and matrix factorization models have been recently proposed to tackle the same problem with more competitive performance. In this paper, we try to answer whether these highly complex deep learning models are without alternative.
  arXiv  Detail & Related papers  (2021-01-06T16:18:04Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Using Machine Learning to Emulate Agent-Based Simulations [0.0]
  We evaluate the performance of multiple machine-learning methods as statistical emulators for use in the analysis of agent-based models (ABMs) We propose that agent-based modelling would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for the models.
  arXiv  Detail & Related papers  (2020-05-05T11:48:36Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.