A Wavelet-Based Deep Learning Framework for Predicting Peak Intensity of Hurricanes in the Atlantic Ocean

Every year, hurricanes pose a serious threat to coastal communities, and forecasting their maximum intensities has been a crucial task for scientists. Computational methods have been used to forecast the intensities of hurricanes across varying time horizons. However, as climate change has increased the volatility of the intensities of recent hurricanes, newer and adaptable methods must be devised. In this study, a framework is proposed to estimate the maximum intensity of tropical cyclones (TCs) in the Atlantic Ocean using a multi-input convolutional neural network (CNN). From the Atlantic hurricane seasons of 2000 through 2021, over 100 TCs that reached hurricane-level wind speeds are used. Novel algorithms are used to collect and preprocess both satellite image data and non-image data for these TCs. Namely, Discrete Wavelet Transforms (DWTs) are used to decompose individual bands of satellite image data, eliminating noise and extracting hidden frequency details before training. Validation tests indicate that this framework can estimate the maximum wind speed of TCs with a root mean square error of 15 knots. This framework provides preliminary predictions that can supplement current computational methods that would otherwise not be able to account for climate change. Future work can be done by forecasting with time constraints, and to provide estimations for more metrics such as pressure and precipitation.

Decision-Making Models Based on Meta-Reinforcement Learning for Intelligent Vehicles at Urban Intersections

Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections.To deal with this,a deep deterministic policy gradient(DDPG)decision-making algorithm(T-DDPG)based on a time-series Markov decision process(T-MDP)was developed,where the state was extended to collect observations from several consecutive frames.Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm.Furthermore,model-agnostic meta-learning(MAML)was incorporated into the T-DDPG algorithm to improve the training method,leading to a decision algorithm(T-MAML-DDPG)based on a secondary gradient.Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models.The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios,which could improve traffic safety and efficiency.The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.