Ensemble deep learning models for tropical cyclone intensity prediction using heterogeneous datasets

Abstract

The prediction of the Tropical Cyclone (TC) intensity helps the government to take proper precautions and disseminate appropriate warnings to civilians. Intensity prediction for TC is a very challenging task due to its dynamically changing internal and external impact factors. We proposed a system to predict TC intensity using CNN-based ensemble deep-learning models that are trained by both satellite images and numerical data of the TC. This paper presents a thorough examination of several deep-learning models such as CNN, Recurrent Neural Networks (RNN) and transfer learning models (AlexNet and VGG) to determine their effectiveness in forecasting TC intensity. Our focus is on four widely recognized models: AlexNet, VGG16, RNN and, a customized CNN-based ensemble model all of which were trained exclusively on image data, as well as an ensemble model that utilized both image and numerical datasets for training. Our analysis evaluates the performance of each model in terms of the loss incurred. The results provide a comparative assessment of the deep learning models selected and offer insights into their respective prediction loss in the form of Mean Square Error (MSE) as 194 in 100 epochs and execution time 1229 s to forecasting TC intensity. We also emphasize the potential benefits of incorporating both image and numerical data into an ensemble model, which can lead to improved prediction accuracy. This research provides valuable knowledge to the field of meteorology and disaster management, paving the way for more resilient and precise TC intensity forecasting models.