Automatic Maintenance Strategies for Weather Forecast Systems Based on Big Data and AI

Abstract

This paper investigates automatic maintenance strategies for weather forecast systems based on big data and artificial intelligence (AI). Firstly, it delves into the collection and processing of weather data, including critical steps such as data source acquisition, data quality cleansing, and feature extraction and analysis. Secondly, it focuses on the application of AI in weather forecasting, including the utilization of machine learning and deep learning, improvements in weather models and prediction techniques, and algorithm optimization. Subsequently, it discusses automatic maintenance strategies in detail, encompassing the concepts and significance of maintenance, the design of automatic maintenance strategies, and the maintenance process and automation tools. Finally, it looks ahead to future trends in weather forecast systems, emphasizing research directions such as uncertainty modeling, explainable AI, handling extreme events, and climate change studies. This research is expected to drive continuous improvement in weather forecast systems, providing more accurate, real-time, and personalized weather information.