WEATHER FORECASTING USING MACHINE LEARNING

Abstract

ABSTRACT -In the realm of meteorology, accurate and reliable weather forecasting is essential for a wide range of applications, from agriculture and disaster preparedness to transportation and resource management. This abstract introduces a novel approach to weather forecasting utilizing the Prophet model, a cutting-edge forecasting tool renowned for its ability to handle time-series data with inherent seasonality and complex patterns. The proposed weather forecasting system integrates key features of the Prophet model to enhance the precision of predictions. Firstly, the model excels in time-series analysis, capturing patterns and seasonality present in weather data with high fidelity. By leveraging historical weather data, the system learns from past trends, improving its accuracy in forecasting future atmospheric conditions. This historical data integration enables the model to adapt to changing weather patterns and provide reliable predictions over various time horizons. Moreover, the Prophet model inherently provides measures of prediction uncertainty, offering valuable insights into the reliability of forecasted weather conditions. Meteorologists and decision-makers can utilize these uncertainty quantifications to assess the confidence level associated with each forecast, thereby making more informed decisions. Additionally, the model's flexibility in handling data anomalies such as missing data and outliers ensures robustness against common challenges encountered in weather datasets. This graceful handling of anomalies contributes to the overall reliability and resilience of the forecasting system.