A Hybrid Deep Learning Approach for Accurate Weather Forecasting: A Review and Analysis of Current Trends

Abstract

Weather forecasting plays a vital role in planning and decision-making across agriculture, energy, transportation, and disaster management. With rapid advancements in artificial intelligence, machine learning, and deep learning, traditional numerical models are increasingly being supplemented or replaced by data-driven methods. This paper presents a comprehensive review of modern weather forecasting techniques, analyzing the effectiveness of models like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR). We discuss their applications across varying temporal and spatial scales, along with the benefits of hybrid approaches that combine physical models with intelligent algorithms. The literature is examined to highlight model performance based on metrics like accuracy, data adaptability, and processing time. Limitations of current models are identified, and future directions are proposed for real-time, high-resolution weather prediction. A critical discussion of key datasets, performance comparisons, and domain-specific challenges lays the groundwork for future research in meteorological AI systems.