(untitled) (Jurnal Teknologi (Sciences & Engineering), 2025)
Extreme rainfall modelling has gained increased attention in recent decades due to its importance for spatial analysis and risk assessment. Similar to any statistical analysis, stochastic modelling involving extreme data is susceptible to errors due to presence of outliers. However, the precise definition of outliers and extreme events remains vague despite the extensive research on the topic. The current outlier detection method often assumes that the sample data follows a normal distribution, which is implausible for rainfall data due to its positively-skewed and heavy tail characteristics. In this study, we focus on eliminating the presence of outlier in daily rainfall series while ensuring the preservation of observed extreme events through the implementation of Extreme Value Theory. The contribution of this study is two folds; foremost, the Peaks-Over-Threshold (POT) algorithm is demonstrated for outlier detection in univariate rainfall data. Secondly, the study introduces an algorithm for generating synthetic outlier using Gamma distribution. The algorithm's performance was tested in various settings using simulated rainfall data to evaluate its effectiveness and dependability before applying it to real data. The result indicates that the algorithm successfully identified outliers without affecting the extreme daily precipitation values in the sample dataset. This finding will greatly enhance future research by improving data quality management, hence enabling more precise analysis of extreme rainfall events.