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*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
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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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  1. BMKG. 2014. Daftar Istilah Klimatologi. daftar-istilah-musim#sthash.eC4BlOVG.dpuf (acces on 10 October 2021)
  2. Davison, A. C., Padoan S. A., dan M. Ribatet. 2012. Statistical Modelling of Spatial Extremes. Statistical Science, 27(2) : 161-186
  3. Brown, B.M., S.I. Resnick. 1977. Extreme values of independent stochastic processes. Journal of Applied Probability. 14(4):732-739
  4. Davison, A.C., Huser, R., 2015. Statistics of Extremes. Annu. Rev. Stat. Appl. 2, 203–235
  5. De Haan, L. 1984. A spectral representation for max-stable processes. The Annals of Probability. 12(4) : 1194-1204
  6. Dombry, C., Eyi-Minko, F., Ribatet, M., 2013. Conditional simulation of max-stable processes. Biometrika 100, 111-124
  7. Hakim, A.R., H. Yasin dan B. Warsito. 2021. Max-Stable Processes With Geometric Gaussian Model On Ocean Wave Height Data, Journal of Mathematical and Computational Science, Vol. 11 No. 1
  8. Huser, R., Genton, M.G., 2016. Non-stationary dependence structures for spatial extremes. J. Agric. Biol. Environ. Stat. 21, 470-491
  9. Kabluchko, Z., Schalater, M., de Haan, L. 2009. Stationary max-stable fields associated to negative definite function. Ann. Probab. 2>1 2042-2065
  10. Padoan, S. A., M. Ribatet., dan S. A. Sisson. 2010. Likelihood-Based Inference for Max-Stable Processes. Journal of the American Statistical Association, Vol. 105, no. 489, Theory and Methods, 263-277
  11. Ribatet, M., 2013. Spatial extremes: Max-stable processes at work. J. Soc. Fr. Stat. 154, 156–177
  12. Ribatet, M., Singleton, R., R Core Team., 2015. SpatialExtremes: modelling spatial extremes. URL R package version 2.0-2
  13. Smith, R. L. 1990. Max-Stable Processes and Spatial Extremes. England : University of Surrey
  14. Schlather, M. 2002. Models for stationary max-stable random fields. Extremes. 5(1) : 33-44
  15. Xu, G., Genton, M.G., 2016. Tukey max-stable processes for spatial extremes. Spatial Stat. 18, 431–443

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