Hybrid Deep Learning Framework for Predicting Long-Term Climate Patterns Using Satellite Data and Internet of Things Sensors

As climate change is taking place at the global level, it undoubtedly calls for proper long-term forecasting in advance to steer mitigation and policy. The introduction in this chapter presents a new software named the Hybrid Deep Learning Framework. The software is very much able to combine convolutional neural networks (CNNs) with spatial feature extraction and long short-term memory (LSTM) networks with temporal trend analysis. The approach also takes advantage of climate data from multiple sources (satellite images, Internet of Things (IoT) sensor data), such as specifying the exact period of measurements—2000–2023. Examples of data preprocessing are normalization, K-nearest neighbor (KNN) imputation for incomplete data, and feature engineering. The model’s final version was achieved by the Adam algorithm, ϵ = 0.001, and confirmed by root mean square error (RMSE) and mean absolute error (MAE) calculations based on the results obtained earlier. Results show that the predictions are accurate in most of the cases, and the model is well on target with the deviations, not exceeding 0.48°C, 7.5 mm/month, and (23%–60%) in the precipitation and temperature forecast, respectively. The model’s objective is to detect changes in the average global temperature of 0.02°C/year and the precipitation anomalies in the areas most affected by the climate crisis. Their approach is not only a reliable and feasible long-term climate forecasting method, but it could also be a decision tool for consistent urban and renewable energy development, as well as for disaster response coordinators. In conclusion, the newest idea would be to reinforce, utilizing the reinforcement learning process, that the model can offer real-time adaptive forecasts that might be useful in highly dynamic environmental conditions.