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Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Spatiotemporal prediction of aeropollen concentration using tree-based machine learning.

Hyemin Hwang1, Martina S Ragettli2, Marloes Eeftens2

  • 1Department of Environmental Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.

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Machine learning models accurately predict daily pollen counts in South Korea, with site-specific models showing superior performance. Climate factors like temperature and solar radiation are key drivers of pollen dynamics.

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Area of Science:

  • Environmental science
  • Aerobiology
  • Computational biology

Background:

  • Pollen is a significant aeroallergen impacting public health.
  • Pollen concentrations are influenced by climate, air pollution, and vegetation.
  • Climate change is expected to exacerbate pollen season variability.

Purpose of the Study:

  • To develop and compare machine learning models for predicting daily pollen concentrations.
  • To assess the performance of spatiotemporal versus site-specific temporal models.
  • To identify key environmental drivers of pollen dynamics for South Korea.

Main Methods:

  • Utilized four tree-based ensemble algorithms: Random Forest, XGBoost, LightGBM, and CatBoost.
  • Trained models using meteorological data, air pollution metrics, and lagged pollen concentrations (2014-2023).
  • Employed SHapley Additive exPlanations (SHAP) to interpret feature importance.

Main Results:

  • Site-specific temporal models outperformed the spatiotemporal model for predicting pollen concentrations.
  • Lagged pollen concentration, cumulative temperature, and solar radiation were the most influential predictors.
  • Air pollution metrics had limited and taxon-specific contributions to pollen prediction.

Conclusions:

  • Local climatic and pollution factors significantly influence pollen dynamics.
  • Site-specific models can be leveraged for region-specific pollen forecasting and early-warning systems.
  • Findings support data-driven public health preparedness for pollen-related allergies amidst climate change.