Drought and Precipitation level forecast in Iran for water source management based on Combinational Markov Models

Drought is considered as one of the vital natural disasters that can be occurred in any climate. Given that drought incidence is inevitable, so its recognition is of important in order to efficient management of water. In this article drought forecasts in Tehran and Mashhad are considered. To this Markov models have been applied. At first stage after preprocessing of data, according to precipitation features, lowest temperature, highest temperature and wind Markov chains were created and the possibility of its incidence for next year was calculated, then at second stage precipitation level, lowest and highest temperature were forecasted by Markov hidden models so that based on them necessary decisions for water source management can be taken. Suggested method was compared based on error rate and accuracy by standard hidden Markov models and Bayesian network. The results show that suggested method on Mashhad collection data had 14% and 31% and on Tehran collection data 10% and 15% better accuracy compared to HMM and Bayesian network, respectively.