جستجوی مقالات مرتبط با کلیدواژه « markov chains » در نشریات گروه « جغرافیا »

 Land use is a dynamic phenomenon whose temporal and spatial changes are caused by human pressures and development. Knowledge of how to use user changes and their causes and factors over a period of time can be of great interest to planners and managers. The present study seeks to evaluate and extend eight sample villages in Mashhad during 2001-2019 and then predict changes to 2031 and 2046.

The research method is descriptive-analytical and it is applied in terms of the purpose. The required data were obtained through the documentary method; and Landsat 7 ETM + satellites (2001 and 2007) and Landsat 8 OLI satellites (2013 and 2019) were used to map land use and survey changes in the study area. After supervised classification of images with maximum similarity algorithm, Markov chain model was used to investigate land use changes in sample villages. Also, the CA Markov model and the Kappa index were used to predict the trend of changes up to 2031 and 2046 for confidence in classification.

The results showed that most of the user change occurred in the built ranges, and land use has increased sharply, and land and garden land use has fallen sharply, with 1008.67 hectares (241.57%) and 32.28 hectares of unused lands and 1060.55 hectares of fields and orchards, respectively. These changes were more frequent in the villages of Hossein Abad Gharqi, Georgian Sefli and Ghale Siah (Black Castle). According to Markov’s Model, the built area in 2031 and 2046 will change by 110.77% and 113.18%, respectively. Therefore, it is necessary to prevent unauthorized expansion of Mashhad and surrounding villages to gardens and agricultural lands with proper planning.

For the first time, the process of land use changes in rural settlements, especially peri-urban settlements, is studied and predicted by satellite imagery.

In recent years, the urbanization, use and land cover of cities, especially the adjacent residential areas located on the periphery of the city, have been affected by human activities. In order to be aware of the changes process as well as to optimize the management of future user changes in order to properly plan the combination of Markov chain and automated cells is one of the best methods and tools that shows the changes in past years and also with regard to past changes. Predicts changes for the future. Considering the fact that in recent years Kermanshah City has had significant changes in land use. Therefore, in order to find out the type and extent of the changes in the area above, the Landsat 7 and 8, 1993, 2003, 2013, 2018 have been used. After visualization, Fuzzy Artmap and Crosstab comparisons were used to detect and evaluate the changes. Also, to predict the process of change, by March 2033, a combination of Markov chain and automated cells has been used. The results of this research showed that over the past 25 years, about 34.42% of the change in utilization has taken place in the studied area, mainly due to human activities, including the expansion of settlements and lands built on agricultural land In all directions around the city of Kermanshah.

In recent decades, cities of Iran have undergone remarkable changes, as in many third world cities, so that growth of urbanization following economic and social changes has led to the unrestrained growth of cities and towns with national and regional role, which in most cities of the country, despite of inappropriate policies of construction and utilization of urban land. Therefore, evaluating its results is essential in future planning of cities. In current study, land use changes and physical expansion around Marand city during the years 1999 to 2016, and then predicted these changes by 2026 with using of Markov chain model. For this purpose, satellite images of Landsat TM, 1999, 2005, 2011 and 2016 have been analyzed. The results show that during this time, built-up land has increased significantly, with about 36% of the changes, and in process of land increasing, land classes of unused land and gardens respectively have played huge function and these are changes chiefly due to an increase in the number of immigrants from settlements around the city of Marand to this city. Moreover, after 10 years, the greatest changes will be made in unused land (26.75%), which will become more to land built, then garden lands (19.52%) will be the largest change toward land built. Indeed, according to results, built up lands have the highest stability and in comparison, unused and garden lands will have low sustainability.

The rapid changes in land use and land cover in the suburbs of large cities of Iran, including the city of Arak, as an industrial metropolitan city, have caused many problems in recent decades, including degradation of natural resources, environmental pollution and poor urban development. In most cases, this has been caused by an unjustified land use change. The study of changes and destruction of resources in past years, the feasibility and prediction of these changes and the pattern of urban growth in the coming years can be an important step in planning and optimal using of resources in the future. In this study, by using Landsat multispectral satellite imagery of 2000, 2018, and 2028, lateral information, Markov chain model, and artificial neural networks, the changes that have occurred in land use and land cover in Arak over the course of 28 years were investigated. To reveal the changes, the conversion rate for each course was obtained in four classes. To investigate the images, they were corrected, and then the best bands were selected for combining the bands. In the following, the classification of rates of maximum probability and evaluation of the accuracy of the classification as well as the overall accuracy were performed with kappa coefficient. Finally, by using the Markov forecasting model and the LCM modeling approach, land use change was predicted for the year 2028. The results indicated the excessive expansion of the built areas, severe degradation of agricultural lands during the period of 2000-2010, sharp decline of barren lands, their conversion to the lands built within the boundaries of the city, and the reduction of mountainous lands in the period of 2010-2018. Modeling the changes also showed that in the period of 2018-2028, the city will grow from the north and northeast to the south and southwest (i.e. toward the mountainous and garden lands of the two villages annexed to the city).

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The physical expansion of cities is a dynamic and continuous process through which the city boundaries and the physical spaces in vertical and horizontal directions are increased both quantitatively and qualitatively. If urban growth is uncontrollable and unplanned, it may negatively affect spatial benefits and ultimately lead to urban expansion. The present study aims to investigate the expansion of Mashhad City in Iran from 2001 to 2017 and then forecast the changes until 2026. A Markov chain is a mathematical and probabilistic method which acts as a random process via which the future state of a pixel depends only on its predecessor and is predicted on it. The direct result of this model is the transmission probability matrix, but in this model no geographic perceptions are obtained. In addition, modeling a single map and representing the spatial distribution of classes is not generated. To solve this problem, the CA-Markov model was designed by John Von Neumann in the 1950s in order to add a spatial feature to the Markov model.
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In the present research, the ETM images of the 2000 and 2009, and OLI images of 2016 from the Landsat Satellite were employed. The images were taken in 28.06.2000, 6. 6. 2009, and 25. 5.2016, respectively. Initially, Landsat Satellite images were geometric and radiometric corrections to reduce the satellite imagery errors. As such, the study area is separated from the images and it is attempted to classify the satellite data. The method used to classify the information is the monitoring method via which educational samples are used to classify the pixels. This means that by defining the specific pixels of each image for each of the classes, classification is performed in the form of the considered classes. Moreover, the maximum similarity algorithm is used for the classification of monitoring. In this method, the reflective value method and each pixel are unknown. In terms of the variance and covariance of a particular spectral reaction class, it is assumed that the distribution of the data of each class is based on the normal distribution around the pixel average of the given class. Practically, the variance and covariance, and the mean of the various classes of each satellite image are calculated for the classification of phenomena; thus, each pixel belongs to a class whose presence in that class is more likely to occur. In order to find out the changes in land use in the study area (Mashhad City), including gardens and agricultural land uses, built-up areas, grassland, and rangelands, the Markov chain module was employed. In the Markov chains, the land-cover classes are used as the chain states. According to this analysis, we always use two raster maps called case models. In addition, the two maps illustrate the time interval between two the images and the predicted interval in the 1401 horizon in the CA-Markov model. The output of the Markov model also includes the possibility of turning the status and matrix of the converted areas in each class, and finally the images are probably conditional for different land use conversions. In this study, Cohen's kappa coefficient (κ) was used for confirming the classification.
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In this research, changes in land cover in gardens and agricultural land uses, and built-up areas, meadows, and rangelands using satellite imagery in a period from 2000 to 2016, and also maximum similarity algorithm, monitoring method and Markov chain model were employed. In the Markov chain model, the cover classes are used as the chain states. In fact, the transmission area matrix represents the number of pixels which are converted from a class to another and has changed from any land-use to another within the same time since 2000-2016.
Based on the CA-Markov model, four floors of the land cover in the land uses mentioned in the strategic perspective document for 1404 (2025) were forecasted. In this regard, it was determined that during the years of 2016, 2009, and 2000, the areas of land ​​uses of the built-up areas were significantly increased, and also the rangelands were expanded relatively; however, the areas of ​​ gardens and agricultural land uses have severely decreased. In addition, the areas of ​​meadows have decreased. The area of ​​land uses in the strategic perspective document for 1404 (2025) is somehow similar to those in 2016; therefore, the gardens and agricultural land uses will change as 90.74%, built-up land uses as 121.57%, meadows as 26.21%, and rangelands as 100%. 
In fact, it was determined that the largest area of ​​transmission in different land uses was in the period between 1999 to 2009 for grasslands, gardens and agricultural land uses, built-up areas, and meadows. In addition, the highest probability of transmission area is in the period from 2000 to 2009 is related to meadows, rangelands, gardens and agricultural land uses, and built-up areas, respectively. The largest transmission area of the mentioned land uses is from 2009 to 2016, which is related to meadows, rangelands, built-up areas, and gardens and agricultural land uses, respectively. The highest transmission area of land uses is within the time interval of 2009-2016 related to meadows, rangelands, built-up areas, gardens and agricultural lands, respectively. It is also revealed that the area of ​​gardens and agricultural land uses was expanded in 2009 as compared to that of 2000, whereas it was significantly reduced in 2015.
The area of the built-up areas during the mentioned years was expanded, and in 2016 (as 359973900 m2) it was significantly increased. Meadows’ area was decreased in 2009 as compared to 2000, but it increased in 2016 as compared to 2009. In 2009, the area of rangelands was decreased as compared to 2000; however, it was again increased significantly in 2016 as compared to the years of 2009 and 2000.
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According to the results obtained from the maps, in the three years of 2000, 2009, 2016, the most changes were related to built-up areas. Therefore, during this period the construction and physical growth of the city were mostly in the northwest direction. Moreover, because constructions usually are done on gardens and agricultural land uses, the decrease in the area of gardens and agricultural land uses, and consequently the increase of the built-up areas can be observed in this part of the city. According to the map of 2016, the gardens and agricultural land uses still remains in the south-east part of the city. One reasons for this issue may be the lack of growth of the city in this direction. One of the important issues in the field of urban planning is how urban spatial development and its resulting pattern are. The pattern derived from the spatial distribution of urban human activities called urban form is changing due to the dynamic and changing nature of cities. Urban growth is horizontal and vertical, and Mashhad has a horizontal urban growth. This form of urban growth over time has led to the disappearance of gardens and agricultural land uses and their transformation into built-up areas, resulting in economic, social and environmental crises. Therefore, land use management planning is one of the most important issues in Mashhad. If land use management planning is done correctly, optimally and accurately, many urban issues and problems may be solved.

Changes resulting from changes in land use and land cover is one of the most important factors in the instability of rural settlements in recent decades. Monitoring of land cover pattern and simulation of future changes is an essential and vital issue for Planning of Ecological Resource Management. this issue in natural environment of guilan due to unique forest conditions is crucial in the sustainability of rural settlements.
This study analyzed changes in land cover of rural areas at East guilan (AstanehAshrafieh, Siahkal, Lahijan & Langerood with area of 2260 km2) over 3 periods. 1989-TM, 2000 –ETM & 2015 land sat 8 images were processed. In order to prediction of changes in land cover untill 2030probability matrix of changes in 6 classes (including urban, rice field, gardens, water source, forests & lands without cover) was calculated. all analyses were performed using markov chain model & cellular automata. Results of changes detection between 1989- 2015 show that area of rice fields, forests & lands without cover has been decreased, but area of urban regions & gardens has been increased.
According to these results, instability of rural settlements will escalate, if there is no fundamental action in making policy for conservation of resources. Results of simulation of future confirms this issue as well. Assessments predict that until 2030, area of rice fields will be reduced 800 hectares. area of urban regions and gardens will be increased respectively 5300 and 4500 hectares.

Nowadays, land use changes are considered as one of the main issues of research in the field of global environmental change and sustainable development. Currently, land use change detection and modeling using satellite imagery is considered as a useful tool for understanding environmental changes related to human activities. The Landsat images 5 and 8 in 2000 and 2014 respectively were used. Fuzzy supervised classification method was used to classify the images. Finally, the changes in land use were identified for 14 years in the foreseeable future and the changes between the 2000 and 2014 were also detected. The results indicated that irrigated Farming land and dry farming lands will be decreased and the barren lands and urban areas will be increased. The irrigated Farming lands, orchards and dry farming lands included 30.9 percent of the study area in 2000; however, they will reduce to 18.4 percent in 2028, urban and industrial areas included 6.7 percent, but will be increased to about 11.5 percent in 2028.

In this study, using Gorgan station data for the period 2010-1980 was studied precipitation trends. In order to extract precipitation trend the moving average and Mann-Kendall test was used. Also, using by SPI (standard precipitation index) drought period and by Markov chain probability event each of the different states drought or wet period was calculated. Then, using NOAA-AVHRR images fluctuations vegetation affected by the precipitation trend and by Markov chain probabilities alternation for each class were examined. Results showed has occurred in 2001-2007 and 1990-1994 Years wet period and in 1982-1986 and 2005-2010 drought period. Study of drought index showed precipitation trend in same period and has occurred wet period in 2001-2005 and drought period from 2005. According to Markov Probabilistic matrix probability alternation from severe drought to same is 0.6 and severe drought to moderate drought is 0.13. In same trend, have decrease semi-dense vegetation from with 494479 hectares in 1985 to less than 380,120 hectares in 2000. Density and area of Vegetation have increased from 2000-2005, but for the reason of drought decrease recently and with 0.48 probabilities will remain non vegetation class in same conditions. The most probability changing is related to sparse vegetation to non-vegetation with 0.41. On this basis, may be will changed 214060 hectares to non-vegetation.

Earth's climate during the twentieth century, especially the past two decades, lost his balance and is more inclined to increase the temperature This can cause hydrological anomalies, and problems in the agricultural sector will evaporate faster. In the present study, the occurrence of heat waves with different continuity in Kermanshah province with use Markov Chain and long-term statistics of daily maximum temperature is 20-year period (1372-1391) has been performed. Statistical data daily maximum temperature for six synoptic stations in at least 20 years of continuous daily statistics were selected. And with an index on the statistical data of all stations, the maximum temperatures that were higher than the index, as the heat wave were introduced. After extracting the data from each station were divided into two categories: short-wave and long-term They will then review process using Markov chain  from identifing and duration of heat waves were analyzed Back. Results obtained showed that the heat wave occurred in Kermanshah province in July and August most of the incremental change in this period has. It is becoming more established, especially in 1380 reached its highest level. Finally, the estimated incidence of 1 to 9 days of heat wave the study was performed in plants

The purpose of this study was to investigate the heat wave's Ahvaz city. In this study, the daily maximum temperature of Ahwaz Station data from 01.01.1340 to 29.12.1386 (17,166 days) was used to analyze the heat waves. First, based on a standardized data matrix and by using Fumiyaki index, deviation matrix of normalized temperature at Ahwaz was prepared. For identifying heat waves , by using computer programming in Matlab Software the heat waves was defined as the days with a continues heat of at least 3 days and its temperature is of standard deviation or more than average. So, the 87 waves of 3 - 14 days were identified and classified. Finally, the continuation of heat wave in Ahwaz according to the laws of probability in the form of random processes and by using Markov chain techniques was analyzed. The results in this research showed that short-term heat wave events are more frequent, the long heat wave has least events. The heat waves are more frequent at the beginning of winter and at the beginning of the fall. Besides, the highest continuation is related to winter. Average number of heat waves days in Ahwaz was 9 days, the highest number of days with a 38-day heat wave observed in 1377. The results of the transition probability matrix based on the maximum likelihood method, showed that winter heat wave is most likely to occur. The results also showed that winter and summer, respectively, have the shortest and longest return period.

One of the most important issues in arid and semi-arid regions is water resources management. Thus the study of the occurrence or non-occurrence of precipitation behavior can improve management water resources in these areas. In this study, the occurrence of days with precipitation in the south west of Iran using daily rainfall synoptic stations in the 1994-2009 periods and adapting Markov chain was performed. The frequency matrix, switching matrix and stable matrix as well as continuous rainfall and the rainfall return periods of 2 and 5 days were calculated. The results show that the minimum probability of rain is in the plain area while the highest probability of rainfall belongs to the mountainous area. Also the highest probability of monthly precipitation is in January and the minimum is that of summer. The minimum of return period and the maximum continuous rainfall are in the mountainous stations and the maximum of return period and minimum continuous rainfall are in the plain area. Also unlike the other months, the maximum of probability rainy days in June is in eastern parts of mountains.

Topographic conditions of the ground surface are intensively effective on the climatic moderate phenomena. Etangs are considered more or less the focal point of accumulation of cold air during the night and may cause the appearance of air temperature collapse. Normal of the air temperature in synoptic and climatological stations shows such conditions for Gavkhoni etang. In this research, Markov Chain model was used for determining the probability of occurrence of days with settlement of cold air in Gavkhoni etang in different seasons of the year. Therefore, the minimum difference of daily air temperature at the two climatic stations of Varzaneh and Isfahan in statistical period (2005-1986), was obtained And with respect to its sign (negative or positive), the days of the year were divided in two categories of ordinary days with code (zero) and the days with accumulation of cold air in etang with code (1). The results showed that %71. 5 of the days of the year had the settlement of cold air in Gavkhooni etang. The number of the days with accumulation of cold air in the autumn, winter, spring, and Summer were %79. 5, %73. 8, %71. 9 and %61. 1respectively. In cases of conditional transition of temperature, the probability of p11occurance is more than other conditions (،، ). Regression relation between the observed and estimated values of n periods of cold air settlement shows that the considered accuracy and reliability for all seasons is more than 99%.