Innovative Machine Learning Applications in Weather Forecasting and Long-Term Climate Analysis

Prof. Pankaj Pali, Prof. Saurabh Verma

International Journal of Innovative Research in Science Engineering and Technology · 25 May 2023

In one sentence. Review of ML for weather prediction and climate analysis; claims a 97% accuracy model but gives no method detail to verify it.

Abstract

The precision of weather forecasting and climate analysis is crucial for various sectors, including agriculture, disaster management, transportation, and public health. While traditional meteorological methods, which primarily depend on physical models and historical data, have made considerable progress, the increasing complexity of atmospheric phenomena and the massive volume of data generated from diverse sources necessitate more advanced approaches. In this context, machine learning (ML) has emerged as a transformative technology, providing innovative techniques to enhance predictive accuracy and gain deeper insights into climatic patterns. This research delves into the numerous applications of machine learning in weather prediction and climate analysis, offering a thorough review of current methodologies and notable achievements. The proposed ML model achieves an impressive accuracy of 97%, with a mean absolute error (MAE) of 0.407 and a root mean square error (RMSE) of 0.203. These findings underscore the model's effectiveness in improving both short-term weather forecasts and long-term climate predictions, thereby enhancing preparedness and mitigation strategies for climate-related risks. Moreover, the integration of ML with advanced technologies, such as cloud computing and big data analytics, further enhances its potential, establishing a robust framework for comprehensive weather and climate analysis. This paper also addresses the challenges and future directions in this rapidly evolving field, highlighting the essential role of advanced analytics in managing the complexities of weather and climate phenomena.

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