On the statistical learning analysis of rain gauge data over the Natuna Islands

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.29244/ijsa.v6i2p347-357. This is version 1 of this Preprint.

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Sandy Hardian Susanto Herho , Faiz Rohman Fajary, Dasapta Erwin Irawan


This article presents state-of-the-art statistical learning methods for analyzing rain gauge data over the Natuna Islands. By using shape preserving piecewise cubic interpolation, we managed to interpolate 671 null values from the daily precipitation data. Dominant periodicity analysis of daily precipitation signals using
Lomb-Scargle Power Spectral Density shows annual, intraseasonal, and interannual precipitation patterns over the Natuna Islands. Unsupervised anomaly analysis using the Isolation Forest algorithm shows there are 146 anomaly daily precipitation data points. We also conducted an experiment to predict the accumulation of monthly precipitation over the Natuna Islands using the Bayesian structural time series algorithm. The results show that the local linear trend with seasonality model is able to model the value of accumulated monthly precipitation for a twelve-month prediction horizon. The work presented here has profound implications for rainfall observations in this area.




Applied Statistics, Atmospheric Sciences, Longitudinal Data Analysis and Time Series, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Statistics and Probability


observational tropical meteorology, cubic interpolation, Lomb- Scargle PSD, isolation forest, Bayesian structural time series, cubic interpolation, Lomb-Scargle PSD, Bayesian structural time series


Published: 2021-06-08 17:35


CC BY Attribution 4.0 International

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