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All Journal Media Statistika
Rukun Santoso
Department of Statistics, Diponegoro University
Decision Sciences, Operations Research & Management

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MAX-STABLE PROCESS WITH GEOMETRIC GAUSSIAN MODEL ON RAINFALL DATA IN SEMARANG CITY Arief Rachman Hakim; Rukun Santoso; Hasbi Yasin; Masithoh Yessi Rochayani
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.59-66

Abstract

Spatial extreme value (SEV) is a statistical technique for modeling extreme events at multiple locations with spatial dependencies between locations. High intensity rainfall can cause disasters such as floods and landslides. Rainfall modelling is needed as an early detection step. SEV was developed from the univariate Extreme Value Theory (EVT) method to become multivariate. This work uses the SEV approach, namely the Max-stable process, which is an extension of the multivariate EVT into infinite dimensions. There are 4 Max-stable process models, namely Smith, Schlater, Brown Resnik, and Geometric Gaussian, which have the Generalized Extreme Value (GEV) distribution. This study models extreme rainfall, using rainfall data in the city of Semarang. This research was carried out by modeling data using the Geometric Gaussian model. This method is developed from the Smith and Schlater model, so this model can get better modeling results than the previous model. The maximum extreme rainfall prediction results for the next two periods are Semarang climatology station 129.30 mm3, Ahmad Yani 121.40 mm3, and Tanjung Mas 111.00 mm3. The result from this study can be used as an alternative for the government for early detection of the possibility of extreme rainfall.
Co-Authors Arief Rachman Hakim Hasbi Yasin Masithoh Yessi Rochayani