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Time Series Analysis and Modeling of Monthly Rainfall in Saudi Arabia
Mohamed M. A. Elgazzar1, BenBella S. Tawfik2

1Mohamed M. A. Elgazzar* , Associate Professor, Delta Technological University, Egypt.
2BenBella S. Tawfik, Associate Professor, Department of Computers and Informatics, Suez Canal University, Ismailia, Egypt.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 10, 2020. | PP: 1780-1786 | Volume-9 Issue-5, March 2020. | Retrieval Number: D1869029420/2020©BEIESP | DOI: 10.35940/ijitee.D1869.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Managing of water resources is an important future issue. Modeling is fundamental in preparation and organization of water resource system. Forecasting of occasions request identify proper models to be used in this process. Water is the main living source on earth. The most common and fundamental source of water on earth supporting the survival of the majority of life forms is Rainfall. Time arrangement investigation which incorporates modeling and estimating constitutes a instrument of foremost significance with reference to a wide extend of logical purposes in meteorology (e.g. precipitation, stickiness, temperature, sun powered radiation, surges and drafts). The show work applies the Box-Jenkins approach, utilizing SARIMA (Regular Autoregressive Coordinates Moving Normal) demonstrate is utilized to perform brief term estimates of month to month time arrangement such as precipitation. Modeling the past watched precipitation time arrangement values which result in utilized to anticipate long run amounts in agreement to the past. The demonstrate is tried by confirming the past precipitation information. In turn, the research produces a solid future figure. This show is assessed by implies of the AIC-, BIC-, and SBC- demonstrate. 
Keywords: Prediction; Auto Regressive Moving Average Models; SARIMA Models
Scope of the Article: Structural Reliability Analysis