Abstract
Abstract This study employs a Multi-layer Perceptron (MLP) model to predict Sea Surface Temperature (SST) using Sea Surface Salinity (SSS) data collected by NASA over a period of 55 years. SSS is closely related to SST, as both are influenced by similar factors such as solar radiation, evaporation, and precipitation. The accuracy of these predictions is then evaluated through an error analysis, conducted on both annual and monthly scales. The results of this study indicate that the MLP model can effectively utilize SSS data to predict SST. However, it was observed that the model’s predictive performance varies across different seasons and regions. This study demonstrates that the MLP model is an effective tool for predicting SST based on SSS data. By employing a MLP to predict SST based on SSS data, this study contributes to the field of meteorology in several ways. However, further research and optimization of the model are needed to improve its predictive accuracy. Additionally, more data needs to be collected and the model’s performance needs to be validated across a wider temporal and spatial scale.
Published Version
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