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

• URL: http://arxiv.org/abs/2605.21507v1
• Date: Sat, 09 May 2026 16:58:22 GMT
• Title: Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift
• Abstract summary: Atmospheric visibility is a critical variable for transportation safety and air quality management.<n>This study introduces a machine learning framework to nowcast visibility in six major South Korean cities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 pollutants, as well as the rarity of low-visibility events. This study introduces a machine learning framework to nowcast visibility in six major South Korean cities. To handle the imbalance in the 2018-2020 training data, we applied the Synthetic Minority Over-sampling Technique with Nominal and Continuous (SMOTENC) and Conditional Tabular Generative Adversarial Network (CTGAN). An ensemble approach combining machine learning and deep learning models was then used and evaluated on a 2021 test dataset. The results revealed a marked decline in predictive performance in the test set compared to the cross-validation phase. This degradation was attributed to a distributional shift between training and testing periods, which was quantitatively confirmed by measuring the Wasserstein distance of the most influential feature identified by SHAP analysis. In general, this study presents a methodology that aims to simultaneously address the dual challenges of data imbalance and temporal distributional shifts, and emphasizes the necessity of accounting for evolving external environmental factors when implementing nowcasting models on time-series data.

Related papers

• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria [0.44198435146063364]
  This study evaluates Long Short-Term Memory (LSTM) networks and the Facebook Prophet model for forecasting multiple pollutants.<n>Results show that Prophet often matches or exceeds LSTM's accuracy, particularly in series dominated by seasonal and long-term trends.
  arXiv  Detail & Related papers  (2025-08-22T09:23:59Z)
• Robust Molecular Property Prediction via Densifying Scarce Labeled Data [53.24886143129006]
  In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel bilevel optimization approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.
  arXiv  Detail & Related papers  (2025-06-13T15:27:40Z)
• 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)
• Precipitation Nowcasting Using Diffusion Transformer with Causal Attention [3.9468501770612576]
  Current deep learning methods fall short in establishing effective dependencies between conditions and forecast results. We propose a Precipitationcasting Using Diffusion Transformer with Causal Attention model.
  arXiv  Detail & Related papers  (2024-10-17T08:10:41Z)
• Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon [0.0]
  This study presents a comprehensive methodology for modeling and forecasting the historical time series of active fire spots detected by the AQUA_M-T satellite in the Amazon, Brazil. The approach employs a mixed Recurrent Neural Network (RNN) model, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to predict the monthly accumulations of daily detected active fire spots.
  arXiv  Detail & Related papers  (2024-09-04T13:11:59Z)
• Statistical Jump Model for Mixed-Type Data with Missing Data Imputation [0.0]
  We introduce the statistical jump model for mixed-type data. This framework incorporates regime persistence, enhancing interpretability and reducing the frequency of state switches. We validate our approach through extensive simulation studies and an empirical application to air quality data.
  arXiv  Detail & Related papers  (2024-09-02T12:36:35Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection [64.4350027428928]
  We propose a novel uncertainty-guided class imbalance learning framework for imbalanced social event detection tasks. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones.
  arXiv  Detail & Related papers  (2023-10-30T03:32:04Z)
• Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction [1.2862507359003323]
  We present a novel deep learning architecture that incorporates theories of spatial statistics into neural networks. DeepLATTE contains an autocorrelation-guided semi-supervised learning strategy to enforce both local autocorrelation patterns and global autocorrelation trends. We conduct a demonstration of DeepLATTE using publicly available data for an important public health topic, air quality prediction in a well-fitting, complex physical environment.
  arXiv  Detail & Related papers  (2021-12-10T03:21:19Z)
• State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting [0.0]
  We present the winning strategy of an electricity demand forecasting competition. This competition was organized to design new forecasting methods for unstable periods such as the one starting in Spring 2020. We rely on state-space models to adapt standard statistical and machine learning models.
  arXiv  Detail & Related papers  (2021-10-01T11:57:37Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)

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.