VISUALISATION TECHNIQUES TO DETECT ABNORMAL SEA SURFACE TEMPERATURE DATA IN THE INDIAN OCEAN
DOI:
https://doi.org/10.46754/jmsi.2026.06.001Keywords:
Sea surface temperature, climatic factors, abnormal data, Chernoff faces, distance-distance plotAbstract
Sea Surface Temperature (SST) is the temperature of the water near an ocean’s surface. It plays a critical role in the interaction between the Earth’s surface and its atmosphere. The factors affecting sea surface temperature are crucial to understanding climate change, while SST itself is influenced by climatic factors such as humidity, air temperature, wind speed, and radiation. In practice, multivariate, anomalous abnormal data cannot be avoided. SST data is no exception to these data inconsistencies and may have multivariate or abnormal data. Therefore, a technique which visualises the abnormal data is crucial. In this study, Chernoff faces, and distance-distance plots are used as visualisation techniques to detect abnormal sea surface temperature data. Chernoff faces is a graphical representation of multivariate data, while a distance-distance plot is a graphical representation to reveal abnormal data. Based on both analyses, the abnormal data are clearly visible of climate factors of SST. Additionally, an Artificial Neural Network (ANN) with an autoencoder was used to detect outliers by applying a threshold to reconstruction errors. The Artificial Neural Network (ANN) model with the autoencoder and Chernoff faces method are effective techniques for detecting outliers in SST datasets.
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