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Evaluating the Effectiveness of Deep Learning Models For Weather Forecasting Compared To Traditional Methods in Improving Predictive Accuracy

  • Unique Paper ID: 175318
  • Volume: 11
  • Issue: 11
  • PageNo: 2282-2289
  • Abstract:
  • This research investigates the application of deep learning models for weather forecasting, focusing on improving predictive accuracy compared to traditional methods. Accurate weather forecasting is critical for various sectors, including agriculture, transportation, and disaster management, where timely and precise predictions significantly impact decision-making processes. The study utilizes advanced deep learning algorithms, such as recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs), to analyze historical weather data and identify patterns that traditional statistical methods may overlook. The research methodology involves the collection and preprocessing of meteorological datasets, including temperature, humidity, pressure, and precipitation records. The preprocessing steps include data cleaning, normalization, and feature extraction to enhance model performance. The study benchmarks the performance of deep learning models against conventional methods, such as autoregressive integrated moving average (ARIMA) and numerical weather prediction (NWP) systems, using standard metrics like mean absolute error (MAE), root mean square error (RMSE), and prediction accuracy. The findings indicate that deep learning models demonstrate superior accuracy and robustness in handling complex, nonlinear relationships within weather data. Additionally, these models adapt effectively to regional variations, showing potential for localized forecasting. The integration of advanced data augmentation techniques and ensemble learning further improves predictive capabilities, making these models suitable for real-time weather forecasting applications. This research highlights the transformative potential of deep learning in modernizing weather prediction systems, providing more reliable and actionable insights. It underscores the importance of adopting innovative technologies to address the limitations of traditional forecasting methods, ultimately contributing to improved planning, resource management, and disaster mitigation across various industries.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 2282-2289

Evaluating the Effectiveness of Deep Learning Models For Weather Forecasting Compared To Traditional Methods in Improving Predictive Accuracy

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