ArticleScientific reports2026
A hybrid artificial neural network-random forest model for enhanced wet bulb temperature prediction in arid urban environments.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate prediction of Wet Bulb Temperature (WBT) is critical for assessing heat stress and guiding early-warning and adaptation strategies in arid urban regions. This study explored advanced machine learning models for monthly WBT prediction in Jeddah City, Saudi Arabia, using long-term climatic observations spanning 1982-2023. Four models, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Random Forest (RF), were systematically assessed against both classical and climatological benchmarks. A seasonal-naïve baseline based on long-term monthly climatology explains 59% of WBT variability (RMSE = 2.38), while a univariate SARIMA model performs substantially worse (RMSE = 22.01; negative skill), highlighting the inadequacy of classical time-series approaches for capturing multivariate WBT dynamics. Among the individual models, RF and ANN markedly outperformed all baselines, with validation R
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