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AI InsightIt was found that this data can be modelled successfully by a first-order Markov chain.
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Özet
The Markov chain is a probabilistic model used with stochastic processes in many branches of science such as meteorology and hydrology. This model is utilized to evaluate persistence and allows the use of combinatorial probability estimates including initial and transitional probabilities. These probabilities contain useful information that can be used in such activities as agriculture, construction (especially in water engineering), industry, tourism, and autdoor community activities such as fairs and athletic events. In this paper, the Markov chain approach was applied to 30 years of daily precipitation data recorded at Göztepe meteorology station. It was found that this data can be modelled successfully by a first-order Markov chain.
Abstract
The Markov chain is a probabilistic model used with stochastic processes in many branches of science such as meteorology and hydrology. This model is utilized to evaluate persistence and allows the use of combinatorial probability estimates including initial and transitional probabilities. These probabilities contain useful information that can be used in such activities as agriculture, construction (especially in water engineering), industry, tourism, and autdoor community activities such as fairs and athletic events. In this paper, the Markov chain approach was applied to 30 years of daily precipitation data recorded at Göztepe meteorology station. It was found that this data can be modelled successfully by a first-order Markov chain.