Despite major improvements in weather and climate modelling and substantial increases in
remotely sensed observations, drought prediction remains a major challenge. After a review …

Understanding and accurately predicting complex three-dimensional (3D) temperature
distributions are critical in diverse domains, including climate science and industrial process …

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and
capability in image generation and dimensionality reduction. The combination of VAE and …

Recent advances in deep learning have provided tools that enable meteorologists to predict
extreme precipitation using massive atmospheric data. However, individual models are …

Background The escalating impact of climate change underscores the critical need for
advanced and sustainable climate forecasting techniques. This review examines the current …

Reconstruction and fast prediction of heat and mass transfer are important for the
improvement of data center operations and energy savings. In this study, an artificial neural …

Unsupervised deep learning methods place increased emphasis on the process of cluster
analysis of unknown samples without requiring sample labels. Clustering algorithms based …

Precipitation is essential for crop production, water resource management, and other
activities. Since precipitation has unpredictable sequential and seasonal characteristics, it is …

Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime
stratosphere. Such extreme events are characterized by substantial disruption to the …

In this paper, we use a variational fuzzy neural network algorithm to conduct an in‐depth
analysis and research on the optimization of music intelligent marketing strategy. The music …