The field of environmental forecasting is moving towards more accurate and efficient detection and prediction of natural disasters such as wildfires and floods. Recent developments in deep learning models, including Vision Transformers and spatio-temporal datasets, have improved the accuracy of wildfire detection and spread forecasting. Additionally, innovative approaches such as the use of Kolmogorov-Arnold Networks and Hierarchical Equal Area iso-Latitude Pixelization have enhanced weather forecasting capabilities. Noteworthy papers include: Wildfire Detection Using Vision Transformer with the Wildfire Dataset, which utilizes a Vision Transformer model for early detection of wildfires. Wildfire spread forecasting with Deep Learning presents a framework for forecasting the final extent of burned areas using a spatio-temporal dataset. PEAR: Equal Area Weather Forecasting on the Sphere introduces a transformer-based weather forecasting model operating on the Hierarchical Equal Area iso-Latitude Pixelization grid. RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting, presents a novel deep learning model for forecasting global river discharge and floods. Localized Weather Prediction Using Kolmogorov-Arnold Network-Based Models and Deep RNNs benchmarks deep recurrent neural networks and Kolmogorov-Arnold-based models for daily forecasting of temperature, precipitation, and pressure in tropical cities.