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ML model struggles with visibility prediction due to data shifts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning framework for predicting atmospheric visibility in six South Korean cities, addressing challenges like imbalanced data and distribution shifts. The study employed techniques such as SMOTENC and CTGAN to handle data imbalance and an ensemble of machine and deep learning models for prediction. A significant drop in performance on the test set compared to cross-validation highlighted the impact of temporal distribution shifts, quantified using Wasserstein distance. AI

IMPACT Presents a methodology for addressing data imbalance and distribution shifts in time-series forecasting, applicable to various scientific domains.

RANK_REASON Academic paper detailing a machine learning methodology for a specific scientific problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ML model struggles with visibility prediction due to data shifts

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Academic paper detailing a machine learning methodology for a specific scientific problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bong Gyun Shin, Chan Sik Lee, Hyesun Suh ·

    Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift

    arXiv:2605.21507v1 Announce Type: cross Abstract: Atmospheric visibility is a critical variable for transportation safety and air quality management, however, accurate prediction remains challenging due to the complex interactions between meteorological conditions and air polluta…

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