Generalized Spatio Temporal Autoregressive Rainfall-Enso Pattern In East Java']Java Indonesia

被引:0
|
作者
Caraka, Rezzy Eko [1 ,2 ]
Ulhusna, Mishbah [3 ]
Supatmanto, Budi Darmawan [4 ]
Goldameir, Noor Ell [5 ]
Hutapea, Bonar [6 ]
Darmawan, Gumgum [7 ]
Novitasari, Dian Candra Rini [8 ]
Pardamean, Bens [9 ]
机构
[1] Bina Nusantara Univ, Bioinformat & Data Sci Res Ctr, Jakarta 11480, Indonesia
[2] Natl Univ Malaysia, Sch Math Sci, Bangi, Malaysia
[3] Univ Putra Indonesia Yptk Padang, Padang, Indonesia
[4] Balai Besar Teknol Modifikasi Cuaca BPPT, Tangerang Selatan, Indonesia
[5] Riau Univ, Kota Pekanbaru, Riau, Indonesia
[6] Tarumanagara Univ, Fac Psychol, Jakarta, Indonesia
[7] Padjadjaran State Univ, Dept Stat, Sumedang, Indonesia
[8] UIN Sunan Ampel Surabaya, Dept Math, Surabaya, Indonesia
[9] Bina Nusantara Univ, BINUS Grad Program, Comp Sci Dept, Comp Sci, Jakarta 11480, Indonesia
关键词
Spatial; Temporal; Rainfall; GSTAR; MODEL;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Monthly rainfall analysis and forecasts are made to be able to provide a clear picture of the rain and climate conditions that have occurred and will occur in the territory of Indonesia. The information can be known in an area that has the most substantial maximum rainfall and in an area which has the lowest rainfall described and presented spatial pattern by using the ArcView-GIS. The purpose of making monthly rainfall forecasts is to be used and utilized by users, especially in the agriculture and irrigation sectors to plan and make decisions within the next 1 to 3 months related to climatic conditions, especially rainfall. In this paper, we use annual rainfall data in six location East Java. We analysis ENSO phenomena as well as rainfall forecasting in January - March 2018 by using generalized space-time autoregressive and get an accuracy MAPE out samp; e amount 2.95% dan RMSE out sample amount 4.77.
引用
收藏
页码:75 / 79
页数:5
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