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Showing 3 results for Markov Chain Model
Analysis of the Spell of Rainy Days in Lake Urmia Basin using Markov Chain Model
Khadijeh Javan,
Volume 16, Issue 43 (12-2016)
Abstract

In this study, the Frequency and the spell of rainy days was analyzed in Lake Uremia Basin using Markov chain model. For this purpose, the daily precipitation data of 7 synoptic stations in Lake Uremia basin were used for the period 1995- 2014. The daily precipitation data at each station were classified into the wet and dry state and the fitness of first order Markov chain on data series was examined using Chi-square test at a significance level of 0.01 and was approved. After computing transition probability matrix, the persistent probability, average spell of dry days and rainy days and weather cycle was calculated. By calculating the frequency of 1-10 rainy, the spell of this periods and 2-5-days return period were calculated. The results show that in this study period the average of rainy days is 25% and the probability of Pdd is more than other states (Pww ، Pdw و Pwd). The average spell of rainy days in the study area was estimated at about two days. Generally, in all stations the persistent probability of wet state is more than rainy state. Estimation of frequency and spell of rainy days and 2-5-days return period show that with increasing duration, the frequency of rainy days decreases. Also with increasing duration of rainy days, their spell is reduces and return period increases.


Modeling land use changes using Markov chain model and LCM model
Dr Mohammad Ebrahim Afifi,
Volume 20, Issue 56 (3-2020)
Abstract
Land use maps are considered as the most important sources of information in natural resource management. The purpose of this research is to review, model, and predict landslide changes in the 30-year period by LCM model in Shiraz. In this research, TM Landsat 4, 5 and OLI Landsat 8 images were used for 1985, 2000 and 2015 respectively, as well as topographic maps and area coverage. Subsequent validation and detection of changes were made using the prediction model of variation The use of LCM markov and the model of user change approach. The images were classified into four classes of Bayer, garden, urban lands, and arable land for each of the three periods. According to the results, aquaculture is the most dynamic user in the area, which has led to an upward trend during 1985-2015, so that the amount (4337 ha, 12.7%) has been added to this area. The Bayer user change trend was also a downward trend during 1985 to 2015, reducing the 99.1995 hectares of this class. The results of the change in the 1985 changes with a kappa coefficient of 0.88, in the 2000 period with a CAAP of 0.77, and in the period 2015 with a Kappa coefficient of 0.92. The results of the change detection in 2030 are such that if the current trend continues in the region, 20.33% will be added to the crop category, so that in 2030, agricultural cropping will be 95.60% of the area of ​​the area Gets In the Bayer and Garden uses 21.22% and 0.21% of the total area of ​​each user has been reduced and has been added to the urban area. The prediction map derived from the Markov chain model is very important for providing a general view for better management of natural resources.


 
Investigating the Development of Urban Space in the face of Environmental Hazards (Case Study of Noor County)
Hossein Sharifi, Mehrdad Ramezanipour, Leila Ebrahimi,
Volume 24, Issue 75 (2-2025)
Abstract
Today, human settlements around the world are exposed to natural hazards for a variety of reasons. These risks, which bring with them a lot of human and financial losses, require preventive measures. The purpose of this study is to investigate the development of urban space in order to deal with environmental hazards in Noor city. The method of this research is also descriptive. Data collection is using library and documentary studies and questionnaires. In order to analyze the questionnaires using ANP method and fuzzy logic method, evaluate each of the criteria and determine their importance coefficients. Based on the results, spatial assessment was performed using ArcGis software and hazard zones were identified. According to the results of risk potential zoning, the northern and southern areas of the city have the highest risk potential. To predict the development of residential areas, the combined Markov chain model and cellular automation were used. The results showed that the continuous expansion of built areas in recent decades has caused rapid changes in land use and the built areas of the city has increased from 2.43% of the total area in 2010 to 3.68% in 2019. The results also showed that regardless of the natural hazards, the built-up areas will increase and as a result of urbanization, the built-up areas will be more prone to high-risk lands. However, if sustainable development policies are fully implemented, cities and built-up areas will be able to maintain their development spaces from high-risk areas for the benefit of the city and its residents.
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