Predicting anomalous weather events using supervised machine learning

The complexity and variability of atmospheric processes make it difficult to predict weather anomalies. Early detection of weather anomalies is critical to ensure that the necessary precautions are taken to limit the impact on people and economic activities. There is a growing interest in the use of machine learning techniques as an alternative to traditional weather forecasting methods. In this study, the use of machine learning techniques to predict daily maximum temperatures and detect temperature anomalies is investigated. Machine learning techniques were trained to predict weather anomalies for three stations in the Gauteng and Northern Cape provinces of South Africa. Three machine learning techniques were selected based on their use and performance in the relevant literature. The techniques include the Support Vector Machine, Artificial Neural Network and Huber Regressor. Both regression and classification-based techniques were evaluated and compared to determine which provide optimal performance for predicting temperatures and detecting anomalies. The regression-based techniques were trained to predict the daily maximum temperatures (for the next day) based on the previous three day's conditions. The predictions were evaluated based on the next day prediction error and the anomaly detection rate in the predictions. Techniques based on classification were trained to classify whether an anomaly would occur the next day based on the previous three day's conditions. The results showed that the machine learning techniques performed well at predicting the next day's maximum temperatures. However, the techniques had a low success rate in detecting anomalies.

Grantor

Department of Computer Science

Year dc:date.issued

2022

Author dc:creator

• Williams, Edwina

Advisor dc:contributor.advisor

• Moodley, Deshen

• Computer Science

Handle dc:identifier.uri

http://hdl.handle.net/11427/36950

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/36950