Adaptive Weather Forecasting using Optimal Neural Network Model

The growing unpredictability of weather events requires more precise and confident forecasting techniques to counter natural disasters and enhance climate resilience. Conventional models such as numerical weather prediction (NWP) and statistical approaches suffer from the complexity of non-linear chaotic systems and an inability to adapt in real-time. Although machine learning approaches such as decision trees and support vector machines provide reasonable accuracy, they are weak in detecting intricate spatiotemporal patterns in meteorological datasets. To overcome these limitations, this research proposes an Optimal Neural Network (ONN)-based weather forecasting model that utilizes deep learning to enhance accuracy and efficiency. The ONN utilizes optimized hyperparameters, sophisticated feature extraction, and adaptive learning to provide robust generalization over a wide range of climatic conditions. Comparative analysis indicates that ONN outperforms traditional NWP and deep learning models in terms of accuracy and computational efficiency, with 94.3% accuracy and a 92.8% F1-score. Its improved performance is due to a real-time adaptive optimization algorithm that dynamically adjusts network parameters to minimize prediction errors. Overall, the ONN-based method establishes a new standard for data-driven weather forecasting, making climate monitoring systems more reliable across the world.