ENHANCING WEATHER PREDICTION ACCURACY USING HYBRID MACHINE LEARNING TECHNIQUES: A COMPREHENSIVE APPROACH

Abstract

Modern life depends on precise weather forecasting because global warming intensifies which affects how people live while handling energy needs and maintaining agriculture and protecting the environment. This study proposes a temperature data prediction system which integrates convolutional neural networks (CNNs) with long short-term memory networks (LSTMs). The CNN-LSTM hybrid model connects two network types to process time sequences and detect spatial information alike.  The hybrid CNN-LSTM combines temporal and spatial processing to generate weather forecasts which are dependable and precise for meteorological data analysis.  Researchers confirm that adding CNN-LSTM technology increases prediction accuracy especially for intricate tasks such as long-range weather forecasting. The combination of CNN and LSTM models brings strong performance in weather forecasting due to its success handling large and diverse meteorological data types. Time-dependent data management through LSTMs produces highly accurate and stable predictions while spatial feature extraction relies on CNNs. During processing of complex meteorological information, the model demonstrates excellent performance by handling problems related to data dimensions and missing values. MAE functions as the chosen loss function in this model. The testing results prove the potential of this climatology prediction model through its ability to produce curves that match test data measurement results. This research establishes essential foundations for future weather prediction systems within global climate change scenarios and provides valuable findings that benefit agriculture as well as energy management and urban development practices.