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MAX-STABLE PROCESS WITH GEOMETRIC GAUSSIAN MODEL ON RAINFALL DATA IN SEMARANG CITY

*Arief Rachman Hakim scopus  -  Department of Statistics, Diponegoro University, Indonesia
Rukun Santoso  -  Department of Statistics, Diponegoro University, Indonesia
Hasbi Yasin orcid scopus publons  -  Department of Statistics, Diponegoro University, Indonesia
Masithoh Yessi Rochayani  -  Department of Statistics, Diponegoro University, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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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.

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Keywords: Rainfall; Geometric Gaussian; Max-stable Process; Spatial.

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