Anomaly Detection in Meteorological Data Using Machine Learning Techniques

Anomaly detection is a very important component in weather forecasting, disaster preparedness and climate modeling in meteorology. We use Density Based Spatial Clustering of Applications with Noise, Isolation Forest, Local Outlier Factor, Elliptic Envelope and One Class Support Vector Machine to detect anomalies in Tunisian meteorological data in this study. Key meteorological parameters, such as wind speed, rainfall and temperature, was analyzed as the important indicators of atmospheric conditions. DBSCAN detected local density changes very well and is therefore a good choice for anomaly detection in areas with complex spatial patterns from the models used. Isolation Forest algorithm was able to find global anomalies with robust performance, giving very high true positive rates with minimum false positives. The use of these machine learning techniques is complementary and this dual approach shows how they can be used to detect different kinds of anomalies. The results show how machine learning can be used to make environmental monitoring and weather prediction much more accurate and reliable, with the ability to detect anomalies precisely and reliably. Having such capabilities is critical for taking proactive actions in response to extreme weather events, and to be able to intervene at the right time to minimize the impacts of the events. Additionally, these models could be integrated into real time monitoring systems to provide additional utility in optimizing their use and provide actionable insight for disaster management and climate resilience. Further research could involve the fusion of these algorithms with deep learning methods, larger datasets, and real time deployment to build intelligent systems that can learn and adapt to changing environmental conditions. This would be a large advance in applying machine learning in meteorology.