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

Aug 3, 2026

The research proposes a hybrid forecasting technique for climate variables, targeting Sea Surface Temperature (SST) patterns in the Antalya region of southeast Turkey. It integrates wavelet decomposition with machine learning to address nonlinearity, nonstationarity, and multiscale dynamics in climate time series. Wavelet components separate high-frequency noise, intermediate-scale variables, and long-term trends, enabling clearer feature extraction. Using a one-way ANN, the integrated pipeline shows empirically significant error reduction versus models without wavelet preprocessing, with wavelet hybrid decompositions reducing error by 10%–30% across seasons.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Climate forecasting challenges

Role of satellite remote sensing

Wavelet analysis for time-frequency decomposition

Hybrid wavelet-ML approach

Study focus and objectives

Enhancing Climate Variable Prediction through Wavelet-Machine Learning Integration and Remote Sensing Data Abdallah DWIKAT1, Zafer ASLAN1 1 Department of Computer Engineering, Faculty of Engineering, Istanbul Aydin University, Istanbul, 34295, Türkiye [abdallahdwikat@stu.aydin.edu.tr](abdallahdwikat@stu.aydin.edu.tr), [zaferaslan@aydin.edu.tr](zaferaslan@aydin.edu.tr) Keywords: Wavelet analysis, machine learning, climate prediction, remote sensing, hybrid models 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. 1. Introduction Accurate and reliable forecasting of climate variables is essential for effective environmental management, disaster preparedness, and sustainable allocation of natural resources. However, climate data—including key variables such as Sea Surface Temperature (SST), precipitation, and wind speed—are characterized by non-linearity, non- stationarity, and multiscale dynamics (Siddiqi et al., 2019), making them inherently challenging to model. These complex data characteristics often limit the effectiveness of traditional machine learning (ML) models, leading to outcomes such asunderfitting, poor generalization, or overfitting when applied to real-world climate forecasting. The advent of global satellite remote sensing technologies has dramatically changed the landscape of climate data collection. Satellite-derived observations provide an unprecedented volume of imagery and continuous, wide-area coverage. For many variables, they offer superior spatial and temporal resolution compared to traditional in situ measurements. Yet, realizing the potential of this vast remote sensing data is only half the challenge; the other is advancing analytical approaches to extract meaningful patterns and predictive insights from these complex and often noisy datasets. By integrating climate modeling methodologies with remote sensing data, we can develop integrative system approaches to improve our understanding of Earth's systems and enhance predictive capacity across diverse regions. In this context, wavelet analysis is a powerful signal processing tool that uniquely decomposes complicated time series into their timefrequency co

FAQs

What forecasting problem does the research address?

It targets accurate forecasting of climate variables, specifically Sea Surface Temperature (SST) patterns, for the Antalya region of southeast Turkey.

How does wavelet analysis improve the machine learning pipeline?

Wavelet decomposition separates nonstationary climate data into time-frequency components, isolating high-frequency noise, intermediate-scale variables, and long-term trends to improve feature extraction and prediction accuracy.

What evidence is provided about model performance improvements?

Results using a one-way ANN show empirically significant error reduction when wavelet preprocessing is integrated, with wavelet hybrid decompositions lowering error by about 10%–30% across seasons.

Aug 3, 2026

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