ABSTRACT
Background: Climate forecasting is a severe issue in environmental science, as the nonlinear interactions between the atmospheric variables and the growing complexity of the environmental data provided by current monitoring technologies create a significant challenge. Conventional statistical forecasting systems frequently have difficulty with the multidimensional nature of climate systems, especially when the environmental data are highly interdependent over time, and also when their features are heterogeneous. Recent advances in machine learning offer novel opportunities for analysis in the modeling of complex environmental processes, as well as enhancing the predictive performance of climate forecasting systems.
Objective: This study aims to evaluate the application of machine learning algorithms in the prediction of climate variability and will also endeavor to establish whether better predictive models can be applied to enhance the accuracy of the forecasts than more traditional statistical methods.
Methods: The research employs a quantitative predictive modeling framework based on environmental observations collected between 2010 and 2024, consisting of 1,260 climate records representing key atmospheric indicators including temperature, precipitation, humidity, solar radiation, wind speed, and evapotranspiration. Several predictive algorithms were implemented, including Random Forest, Gradient Boosting Machine, and Deep Neural Networks. Model performance was evaluated using statistical forecasting metrics such as Root Mean Squared Error, Mean Absolute Error, and the coefficient of determination.
Results: The findings showed the presence of strong variability within key environmental predictors confirms that the dataset contains sufficient dynamic information for effective machine learning forecasting, and thermodynamic variables predominated predictive models of climate variability. Also, the findings proved that machine learning models are more efficient in comparison to conventional regression-based forecasting.
Conclusion: The empirical results proved that machine learning algorithms are much more effective than the traditional regression-based models in terms of forecasting. Neural network architectures and ensemble learning methods were especially effective at the task of nonlinear modeling of the dependencies between environmental variables. Also, such findings suggest that predictive models that were trained with combined environmental data can be used to detect latent relationships in climatic data and provide more precise projections of atmospheric behavior. Finally, models of climate prediction that rely on data and can utilize large volumes of environmental data can thus help in making more efficient environmental planning and policy formulation.
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