Markov Chain Modeling of Stochastic Nature of Rainfall in Nigeria

S.o Amusa, M. S Fatoki, Y.a Ganiyu and A.a Fadiji
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S.o Amusa: Department of Statistics, Yaba College of Technology, Yaba-Lagos State
M. S Fatoki: Department of Statistics, Yaba College of Technology, Yaba-Lagos State
Y.a Ganiyu: Department of Statistics, Yaba College of Technology, Yaba-Lagos State
A.a Fadiji: Department of Statistics, Yaba College of Technology, Yaba-Lagos State

International Journal of Research and Innovation in Social Science, 2026, vol. 10, issue 4, 357-366

Abstract: This study investigates the stochastic behavior of rainfall in Nigeria using probabilistic modeling and Markov chain analysis. Recognizing the critical role of rainfall in agriculture, water resource management, and urban planning, the project captures inherent randomness, seasonal variability, and long-term trends in precipitation patterns. Monthly rainfall data from selected Nigerian cities were analyzed with WinQSB software to develop transition probability matrices for defined rainfall states: low, moderate, heavy, very heavy, and extremely heavy. The study derives transition probabilities, steady-state distributions, and recurrence times for different rainfall intensities. Analysis reveals significant persistence in low rainfall states and varied probabilities for transitions to higher intensities, offering insights into the likelihood of extreme rainfall events. Steady-state probabilities indicate the long-term frequency of each rainfall category, while recurrence times measure the average duration between similar events. Results confirm that Markov chain models are effective tools for predicting rainfall variability in a region characterized by climatic uncertainty. These findings have significant implications for enhancing early warning systems, optimizing agricultural practices, and guiding water resource management policies in Nigeria. Recommendations for future research include integrating hybrid models that combine traditional stochastic methods with machine learning techniques to further refine rainfall predictions and support adaptive climate strategies.

Date: 2026
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