Tropical climate prediction method combining random forest and feature fusion

Guotao Liu

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1900-19

Abstract: Tropical cyclones pose significant threats to coastal populations, causing destruction and loss of life. Precisely forecasting the frequency and arrival dates is still a challenge. This research presents a technique for feature extraction and integration using a random forest (RF) model with a cascaded convolutional neural network. The approach combines different meteorological maps and uses a feature fusion technique to improve prediction accuracy. The RF model is optimized by a grid search algorithm. The results show that the proposed model outperforms conventional models to achieve a mean absolute error of 0.48 and a mean relative error of 14.14%.

Keywords: random forest; feature fusion; cyclone frequency; climate prediction; tropic (search for similar items in EconPapers)
Date: 2025
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