Stochastic Model for Rainfall Occurrence Using Markov Chain Model in Kurdufan State, Sudan

Authors

  • Rahmtalla Yousif Adam Assistant Professor, Department of Statistics, University of Tabuk, K S A

Keywords:

Kordofan, Markov Chains, rainfall, stochastic process, Transition matrix, (, ).

Abstract

This paper will attempt to demonstrate the potential benefits of using Stochastic Processes for modeling and interpreting historical rainfall records by the examination of weekly rainfall occurrence using Markov Chains as the driving mechanism. The weekly occurrence of rainfall was modeled by two-state first and second order Markov chain. While the amount of rainfall of a rainy week was approximated by taking the maximum likelihood estimation method to predict transition probability matrices of rainfall sequences during the rainy season. Daily rainfall data for 21 years was collected from two meteorological stations located in Kurdufan State (Sudan). The data indicated that the season starts effectively, on 8th week of June at El-Obied station and sixth week of June at Kadugli station. The transition probability matrix of Markov chain model found to be homogeneous and remained constant over the period study. Accordingly, the Index of Drought-proneness degree (ID) was found to be higher in Elobied than Kadugli Station and the hypothesis is accepted at 5% level of significant with P-value (0.151).

References

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Published

2016-03-12

How to Cite

Adam, R. Y. (2016). Stochastic Model for Rainfall Occurrence Using Markov Chain Model in Kurdufan State, Sudan. American Scientific Research Journal for Engineering, Technology, and Sciences, 17(1), 272–286. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/1454

Issue

Vol. 17 No. 1 (2016)

Section

Articles

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