Showing 5 results for Markov Chain
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.
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Volume 17, Issue 47 (12-2017)
Abstract
Suspended particles management is one of the important issues in controlling the air pollution of cities. These particles cause and develop heart and respiratory diseases in people. Mashhad is considered as one of the main and populous cities of Iran. Because of its climatic conditions and its tourism, the city is at the highest risk of this type of pollution. We attempted to use the multi-layer perceptron (MLP) artificial neural network and a Markov chain model to predict PM10 concentrations the city. We applied hourly data of CO, SO2, PM2.5 and temperature in late March and April 2015. Out of 1488 data series, 1300 data were used for network training and 188 data were used for validation. The results indicated the optimal performance of these methods in predicting of the amount of pollutants and also the probability of occurrence of hours with different quality of contamination. The best MLP artificial neural network model predicted the amount of pollutant particles with a coefficient of determination (R2) 0.88, index of agreement of 0.91 and a mean square error of 2.26. Also, the Markov model with average absolute error predicted about 0.1 percent of the probability of transferring the condition and the continuation of different states of air pollution caused by suspended particles.
Fatemeh Mohammadyary, Hamidreza Pourkhabbaz, Hossin Aghdar, Morteza Tavakoly,
Volume 18, Issue 50 (6-2018)
Abstract
Land-use change is one of the most important challenges of land-use planning that lies with planners, decision-makers and policymakers and has a direct impact on many issues, such as economic growth and the quality of the environment. The present study examines the land use change trends in Behbahan city for 2014 and 2028 using LCM in the GIS environment. Analysis and visibility of user variations, carried out in two periods of Landsat satellite images of 2000 (ETM + sensor) and 2014 (OLI sensors), and land cover maps for each year. The transmission potential modeling was performed by using the multi-layer perceptron artificial neural network algorithm using six independent variables and the distribution of changes in user usage were calculated by Markov chain method. The results of the prediction showed that the most reduction in the changes is the degradation of the rangelands and the greatest increase in the area of agricultural use. According to the horizontal tabulation results of the 2028 map, it can be stated that from the total area of the area 28336.22 hectares of land were unchanged and 33223.78 hectares of land use change. Also Rangeland and forest degradation during this time period can be a danger to urban planners and natural resources.
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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.
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.
