Predicting Rainfall Pattern in Kakamega County using Time Series

Joan Awuor; Elvis Kemboi; Benson Nzaro; Hesbon Mocha

doi:10.51867/Asarev.Maths.1.1.6

Authors

Joan Awuor Department of Mathematics, Masinde Muliro University of Science and Technology, P. O. Box 190-50100, Kakamega, Kenya. Author
Elvis Kemboi Department of Mathematics, Masinde Muliro University of Science and Technology, P. O. Box 190-50100, Kakamega, Kenya. Author
Benson Nzaro Department of Mathematics, Masinde Muliro University of Science and Technology, P. O. Box 190-50100, Kakamega, Kenya. Author
Hesbon Mocha Department of Mathematics, Masinde Muliro University of Science and Technology, P. O. Box 190-50100, Kakamega, Kenya. Author

DOI:

https://doi.org/10.51867/Asarev.Maths.1.1.6

Keywords:

Rainfall Patterns, Predictability, Optimizing Irrigation

Abstract

Rainfall patterns play a critical role in shaping various aspects of our lives. Understanding the patterns, trends and predictability of rainfall is essential for effective planning and decision making in various aspects including agriculture, water resource management, disaster preparedness and social economic planning. In agricultural activities crops require specific amount of water at the right time for growth. By understanding the rainfall patterns, farmers can adapt their farming activities, optimizing irrigation strategies and make informed decisions. In the management, it help policy makers and management authorities for planning efficient water allocations and conservations measures. Therefore, in this paper we fit a time series model that best describes rainfall patterns of Kakamega county for the general ARIMA and generated the values of (P, D, Q) to forecast average expected monthly rainfall. Also we use R software for verification and data fitting of the model. The data we have used is from the Kakamega meteorological station in Kakamega.

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