Enhancing Climate Variable Prediction through Wavelet-Machine Learning Integration and Remote Sensing Data

Abstract. This research establishes a new technique to effectively forecast climate variables, specifically Sea Surface Temperature (SS T) patterns for the Antalya region of southeast Turkey. The technique combines wavelet decomposition methods with advanced machine learning techniques to consider the many complexities that climate time series data adds to the task of forecasting. The separate wavelet components allowed us to decompose an intricate, nonstationary climate dataset into many of its temporal components that include high-frequency noise (d1), intermediate scale variables (d2), and long-term temporal trends (d3). Obviously, the disentanglement of different types of temporal variation improved the extraction of feature classes and ultimately made whatever machine learning modelling more accurate and reliable. With the one-way ANN, we examined the performance of machine learning models with wavelet pre-processing and without and reported an empirically significant reduction in error when the pipeline integrated these steps. We also demonstrated how remote sensing makes our vast area, expanding temporally and spatially, suitable for a broad range of geospatial applications. The results will provide guidance in the areas of regional climate research, emergency preparedness, and for making agricultural decisions, while showing how complementary approaches to satellite observations, utilizing signal processing techniques and machine learning can collectively contribute to improved environmental data monitoring and prediction. This research is spatially focused within the established bounds of a particular climate region and provides a detailed account of the machine learning methods used for recognition’s sake. The Wavelet decompositions (Hybrid) decreased the error percentage with a range of 10%-30% in different seasons.