Akademik Veri Yönetim Sistemi
4. INTERNATIONAL NEW YORK ACADEMIC RESEARCH CONGRESS, New York, Amerika Birleşik Devletleri, 15 - 16 Ocak 2022, ss.33, (Özet Bildiri)
Weather forecasting has an important place in human life. Uncertainty in the forecast, uncertain weather,
can cause confusion. Therefore, for centuries, people have made weather forecasts to avoid confusion
and uncertainty (Stephens et al., 2012). In the past, people made predictions using their own methods
and observation. Knowing the weather has the ability to facilitate many situations, from people's travels
to their vacations and daily chores (Hares et al., 2010). In this study, the Markov chain of meteorological
temperature data of the last 30 years obtained from the Regional Directorate of Meteorology in Van
province, special cases and analyzes of the Markov chain were examined and tried to be modeled with
stochastic processes. By using Matlab program, by classifying the received data according to their
ranges, it was determined which class it belongs to for each data in the data set. Transition matrix was
created over the classes, and probability transition matrix was calculated with the transition matrix. With
the obtained probability transition matrix, the weather temperature forecast of Van for the next month
was made. In the V matrix obtained at the end of the study, the average temperature expected in the first
month of the next year was the 2nd case with a probability of 63%. As a result of this, the temperature
range that is valid in the 2nd case is determined as [0°C-20°C]. Making temperature predictions for the
following months, seasons or years with the help of the graphs obtained and the predictions that can be
made with possible Markov processes, will be beneficial for our country in terms of being prepared for
expectations and struggling with many events from prospective public-private investment planning to
heat and drought-related thirst and their consequences. . Based on this result; It adds positive value to
our lives with the expectation of temperature and weather changes due to climates, making it easier to
predict the situation. This provides added value for us. In this context, Markov processes make a visible
contribution to science and, accordingly, to life.