Weather conditions are closely related to human health, yet effective methods for communicating the joint health risks associated with weather-related factors remain limited, especially when accounting for the complex interactions among weather expos...
Integrating disease severity with real-time meteorological variables and advanced machine learning techniques has provided valuable predictive insights for assessing disease severity in wheat. This study emphasizes the potential of machine learning m...
Weed species that escape control (hereafter called residual weeds) coupled with changing weather patterns are emerging challenges for snap bean processors and growers. Field surveys were conducted to identify associations among crop/weed management p...
Accurate and interpretable solar power forecasting is critical for effectively integrating Photo-Voltaic (PV) systems into modern energy infrastructure. This paper introduces a novel two-stage hybrid framework that couples deep learning-based time se...
BACKGROUND: Road traffic accidents (RTAs) are a major public health concern with significant health and economic burdens. Identifying high-risk areas and key contributing factors is essential for developing targeted interventions. While machine learn...
Air pollution remains a critical public health and environmental challenge, particularly in urban areas where traffic emissions and meteorological conditions strongly influence air quality. While Machine Learning (ML) techniques have been increasingl...
BACKGROUND: The global malaria burden is characterized by economic, geographical, and climatic disparities, especially in sub-Saharan Africa (SSA). Moreover, meteorological factors have become increasingly important to understand the malaria burden i...
This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM over Greater Bangkok (G...
International journal of environmental research and public health
Apr 11, 2025
Environmental disasters are becoming increasingly frequent and severe, disproportionately impacting vulnerable populations who face compounded risks due to intersectional factors such as gender, socioeconomic status, rural residence, and cultural ide...
Accurate forecasting of blowing snow events is vital for improving numerical models of snow processes, yet traditional predictive methods often lack interpretability. This study leverages eXtreme Gradient Boosting (XGBoost) to detect blowing snow eve...
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