Integrating CNN-LSTM and Physical Models for Predicting Climate Extremes

Because of their unpredictable nature and the shortcomings of current models, it is extremely difficult to make reliable predictions about severe weather occurrences like heat waves, droughts, cold spells, heavy rain, and storms. Access to accurate forecasting technologies is crucial for stakeholders and legislators in light of the fact that the occurrence and severity of such catastrophes are on the rise due to climate change. In light of the critical need to develop new methods for predicting climatic extremes, this work presents a hybrid approach that combines deep learning with signal processing. When it comes to feature extraction, higher-order Daubechies wavelets inside the DWT architecture perform better than lower-order variations. To further optimise feature selection, a wrapper-structured CFS is also employed. In order to grasp the spatial and temporal patterns in time series data, the predictive model makes use of a CNN coupled with LSTM units. While LSTM models temporal dependencies and erratic trends, CNN extracts inter-variable relationships. A high prediction accuracy of 94.87% was achieved by the suggested CNNLST model in experiments, showing that it is useful in forecasting residential energy demand and related climate extremes. In this age of rapidly increasing climatic variability, this strategy has great promise for improving readiness and policy preparation.