Deep Learning for Weather Forecasting, accepted applied data science of KDD 2019

Deep Learning for Post-Processing Ensemble Weather Forecasts

Weather forecasting with Deep Learning

Develop a deep learning model capable of predicting traffic flow in urban environments. The model will utilize historical traffic data, weather conditions, and road configurations to forecast traffic patterns. This information can be invaluable for traffic management systems, helping to optimize traffic signals and reduce congestion, ultimately.

Research all papers and codes related to weather forecasting based on artificial intelligence methods, including but not limited to deep learning, generative adversarial, multimodal and other strong artificial intelligence

DeepWeather is a deep learning tool that improves weather forecasting accuracy by analyzing satellite images.

Forecasting the output power of photovoltaic (PV) arrays using 3 & 6 years of weather and PV output power data using both machine & deep learning

Official repository for the wind forecasting experiments reported in the paper "PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning" (2025)

CS230 Deep Learning project forecasting PM2.5 pollution using weather data

code developed for the paper "Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning"

Using a hybrid spatial-temporal deep learning approach to forecast subseasonal weather across 514 Western USA geographical regions

Learn how to use FourCastNet, a weather model based on deep learning, to obtain short to medium-range forecasts of crucial atmospheric variables such as surface wind velocities.

Deep Learning for land, oceanic and atmospheric climate variable forecasts

A Deep Learning Approach to Space Weather Proxy Forecasting for Orbital Prediction // A Deep Learning Approach to Solar Radio Flux Forecasting

This repository contains the code and resources for the research project titled "Localized Weather Prediction Using Kolmogorov-Arnold Network-Based Models and Deep RNNs." The project focuses on developing and comparing advanced deep learning models for accurate weather forecasting.

Weather Predction

Weather forecasting using machine learning and deep learning model.

A Deep Learning-Based Traffic Flow Forecasting System that Integrates Weather Data

Forecasting Tomato Bag Quantity Using Predicted Tomato Yield on the Basis of NDVI Values and Weather-Related Variables - Python, R, Deep Learning

Experiment various Deep Learning model (LSTM, RNN, CNN - Conv1D, GRU, and Transformer) to forecast Weather in a specific station and choose the best result

No description available

Repository for paper "A Deep Learning-Based Bias Correction Model for Tropical Cyclone Tracks and Intensity towards Forecasting of the TianXing Large Weather Model"

Deep learning–based solar PV power forecasting using ERA5 reanalysis weather data. Includes population-weighted spatial aggregation, solar geometry feature engineering, and spatiotemporal LSTM modeling.

Deep Learning for Meteorological Forecasting: Advanced Weather Prediction Using LSTM Networks

This project uses deep learning techniques to create a short term weather forecast.

Collection of notebooks illustrating different tools and methods for exploring deep learning applications to global weather forecasting

AI-driven weather forecasting and early-warning system for heatwave and low-visibility detection using ML, deep learning, and explainable AI.

An end-to-end deep learning pipeline for multivariate weather forecasting using a Transformer-based model. Includes: data preprocessing, training, prediction, and an interactive FastAPI web interface.

Using deep learning models, like CNN, ANN, and LSTM we predict factors like CO2 emission, fuel consumption, ETA, peak period, residual resistance, weather conditions, speed trim, and wave forecasting to optimize ship performance.

A deep learning framework for time series forecasting using attention mechanisms. This repository provides an implementation of state-of-the-art neural architectures designed for accurate and interpretable time series prediction tasks such as energy consumption, stock price, weather data, and more.