Sea Surface Temperature (SST) parameter plays an important role in climate change. It can be used as an indicator in detecting various marine phenomena, such as the monsoon phenomenon, the Indian Ocean Dipole (IOD), and the Madden Julian Oscillation (MJO). Several climatic elements are affected by SST, including air temperature, rainfall, wind speed, solar radiation, and relative humidity. Analysis to determine future SST is needed to minimize the impact of SST incidents. Unfortunately, not all those data are complete, thus it would affect the climate change forecasting. Therefore, the detection of the missing data, especially for the climate data, is crucially needed. The purpose of this study is to identify the missing data of SST in Indian Ocean and to predict the amputated data by using generalized additive model (GAM) with median as measurement for amputation. Results show that the fitting amputated data by using GAM after the imputation data was better and smoother than before the imputation.

detection, missing data, sea surface temperatures, median, GAM.

Received: November 23, 2020; Accepted: December 26, 2020; Published: January 12, 2021

How to cite this article: Miftahuddin, O. Fanny, S. Ichsan, Maharani Abu Bakar and Norizan Mohamed, Detection of missing sea surface temperatures (SST) data in the Indian ocean, JP Journal of Biostatistics 18(1) (2021), 51-66. DOI: 10.17654/BS018010051

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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