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Showing 9 results for Markov Chain
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Volume 12, Issue 38 (4-2012)
Abstract
First order of Markov chain is a process that depends on the previous process. The two-two transition probability which indicates the situation of rainy or non-rainy days should be determined to use the first order of Markov chain. For this study, 49 stations in Fars province were selected, and the transition probabilities of months of November to April were calculated by using the first order of Markov chain, separately and then the spatial variations of the transition probabilities were prepared. These transition probabilities can be used for simulation daily rainfall and different dry day return periods. The results showed that the occurrence probability of rainfall and return periods of different dry day were more in February and March, and in November were lower than the months of December to April. On the other hand, the occurrence probability of rainfall and return periods of different dry day were low in November to April in south of Fars province, and incresed towards the north and north-west of the province. Therefore, the north and north-west of the province are the most appropriate places in the Fars province for dryland farming of different crops such as wheat
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Volume 15, Issue 50 (9-2015)
Abstract
About 90 percent of the world's natural disasters occur in relationship between climate factors and among them, the storm accounts for about 30% shares of this disaster. Thus, given the importance of this issue, in this paper two-state Markov chain was used to analyze and forecast of stormy days in Sabzevar city. For this purpose, the daily wind in Sabzevar station during the period (1390-1350) was obtained. The days of storm (code 1) and non- storm (code 0) were divided into two groups. The results of data processing show that the maximum frequency of stormy days 79 days occurred in 1385. During the study period, April with 113 days of stormy, had the highest frequency, and on the contrary, November with 19 days of stormy, had the lowest frequency. In other words Spring was stormy and Autumn was calm season is in Sabzevar. Seasonal results of Markov probability matrix showed that the occurrence of two consecutive stormy days in all seasons is not more than 20%. The minimum (10%) and maximum (19%) probability occurred in autumn and Spring, respectively. However, the probability of two consecutive non- stormy days in all seasons is not less than 90 percent. Later determined the shortest air cycle with approximate duration of 16 days in Spring and also the largest with approximate duration of 45 days in Autumn have been observed. Also, throughout the investigated period, the one-day and two-day stormy sequences had the highest frequency.
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Volume 17, Issue 58 (9-2017)
Abstract
The purpose of this study is to analyze the continuity of windy days based on Markov chain in Zahedan. In order to identify the windy days, horizontal visibility and daily wind speed data during 1983 - 2011 were obtained from meteorological station in Zahedan. It is called a windy day if the maximum wind speed exceeds 15 meters per second and horizontal visibility is less than 1000 m. Based on Markov model, the matrices of the frequency of windy days observed were constructed and the matrices of the probabilities of transition for months and seasons were calculated.In the next step, the correlation of windy days on each to other, with the durability and homogeneity, was tested. Moreover, the expected frequency of windy days, th e period of windy and non-windy days, and the sequence of the windy n-days for each month were calculated. The observed data showed that 2007 had the highest frequency windy days (78 windy days) and, in contrast, the years 2000 and 1993 had the lowest windy days (4 days). In the seasonal study of the frequency of windy days, it was found out that the maximum windy days occurred in winter (306 days) and the minimum in autumn (57 days). Moreover, March with 123 and November with 8 windy days had the highest and the lowest windy days. An alysis of windy periods of one to seven windy days showed that the maximum Persistence of windy days, occurrd in the late winter and early spring and the minimum was in mid-fall. The maximum wind speed occurred in the spring of 2002 (40 meters per second). The average of the windy days was 24.4 days, with a standard deviation of 19.3 days.
Mostafa Taleshi, Hasan Afrakhteh, Mohammad Ali Rahimipour Sheikhaninejad,
Volume 18, Issue 61 (6-2018)
Abstract
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.
A.m Zahra Mahavarpour,
Volume 18, Issue 63 (12-2018)
Abstract
In this paper the prediction of heat waves have been done using Markov Chain. In order to identify heat waves the max temperature of 1437 stations have been applied. The data covers the period of 1962/1/1 to 2004/12/31. Then these stations have been interpolated on 15 km × 15 km grids and has consisted a matrix in 15695 × 7187. The rows represent days and columns represent the pixels. This index was applied on 15695 and on all the pixels. So a matrix in 15695 × 7187 was consisted. Because the Z above 2 indicates the positive abnormality, this value was chosen as the heat wave. Then the values of P and Q that represent the occurrence and nonoccurrence of heat waves were utilized to predict heat waves over Iran. In general the mean annul distribution of heat waves show that southern parts of the country experience more heat waves than northern parts. The investigation of heat waves in spring with two days of return showed that in many parts of the country the value is 100 days but in regions around Kerman this value is around 2000 days with a significant gradient. The time for the occurrence of heat waves with two days of return in fall is reduced to 50 days in comparison to spring.
Fatemeh Mohammadyary, Hamidreza Purkhabbaz, Hossin Aghdar, Morteza Tavakoly,
Volume 19, Issue 65 (6-2019)
Abstract
Change detection and prediction of land cover change in the overall vision for better management of natural resources, protection of agricultural land around urban areas is very effective long-term policies and strategies. one of the methods used to control the process of changes in land cover and land use planners, is modeling. The land use maps 2000 and 2014 BEHBAHAN city was prepared using maximum likelihood classification. It also uses a Markov chain and logistic regression was 2028 land use map. Mapping modeled after the detection of changes in land use map was made in 2014 and 2028 with the LCM. Assess the accuracy, land use maps in 2000 and 2014 to evaluate the accuracy Kappa coefficient and logistic regression with (ROC) Relative Operating Characteristic was calculated. The results of modeling with logistic regression model showed that in all sub model ROC statistic is more than 94/0 These results show a very good offer logistic regression model in the analysis of changes in land use change modeling The results showed the sharpest decline mainly include changes is destruction of rangelands and convert it to other land uses. The highest increase was seen in the area of agricultural land use. Rangeland degradation process can be alarming for managers and urban planners and natural resources. To prevent the decline of rangeland degradation, the need to provide appropriate and efficient management solutions in the fields of grazing and rangeland restoration and improvement programs.
Mr Mehdi Saadat Novin, Mr Ahmad Reza Ebrahimipoor, Mr Farzam Khosravi,
Volume 19, Issue 65 (6-2019)
Abstract
Development of urban construction and ever-increasing growth of population lead to land use changes especially in agricultural lands which play an important role in providing human food. According to this issue, a proper land use planning is required to protecting and preserving the valuable agricultural lands and environment, in today’s world. One of the ways that can be useful to planning is simulation and prediction of land use change according to effective parameters in changes of the land use over the time. In this study, changes in land use of Bojnourd city (The capital city of North Khorasan province), were considered and studied. In this regard, the CA-Markov model was used. The land use maps of two different periods of time were used to calibrate the models and for validate the model, the “Validate” method (which is a statistical method to validate models). Examination of the output maps which are obtained by CA-Markov model, shows that the most growth in land use is in the built up areas. In 1400, the built up areas would grow 5.3% compared to 1388 and in subsequent periods the growth rate would be 3.5% on average.
Dr Mehrdad Ramazanipour,
Volume 19, Issue 68 (4-2019)
Abstract
Spiring late and autumn early frosts is considered farming concerns. A sudden decrease in ambient temperature not only impede the process of blooming, plant pollination and fruit production, but it makes pathogens and freezing the water in the plant cells. In addition, the threat to economic infrastructure, including transport and dams, as well as an increase in energy consumption. In this regard, the frequency and persistence of early and late frosts in Karaj city were analyzed based on probabilistic laws as stochastic processes by using the Markov chains technique. For this purpose, mean daily minimum temperatures was used for a period of 24 years (1985_ 2008), derived from Karaj station. Since most harmful frosts are related to the start of transitional season and higher possibility, at first the probability matrix for frost and non-frost temperature was calculated based on the maximum likelihood method and then the possibility of stability and daily return period each of the two modes of frost and free-frost calculated for all months. The highest probability for freezing days was in January and equal to 0.87. Then 2 and 5-day return period continuation of frost days was calculated for the all months. April has shortest return period of 0.03 days for 2 days frost and 0.03 days for 3 days frost and December and February had the longest frost continuity that is 0.24 days for 2 days frost and 0.02 and 0.01 days for 3 days frost relatively. Also the probability of frost days of month With repetitions of to showed that December, January, February and March had the maximum probability of frost at . In other words, three occurrences of Frost in these months is more probable than any other probability.
Yousef Darvishi, Omid Hosseini, Zeinab Razaghi,
Volume 23, Issue 83 (10-2023)
Abstract
Introduction
Over time, land cover patterns and consequently land use change fundamentally. In order to make the best use of natural resource capabilities, it is essential to obtain accurate information about land use potentials. Kojoor region was registered as one of the most valuable regions of the country in terms of gene and species diversity in the list of the network of protected areas in the world by the approval of the High Council of Environment in 1967.Therefore, in order to protect this area, not only proper knowledge of human factors, but also the role of natural factors such as vegetation is very important.
Data and methodology
In the present study, Landsat satellite images were used in 7 time periods. EVI, DVI and NDVI indices were used to study and analyze vegetation changes in the study area.
In the present study, based on the modeling objectives, the study of changes in forest cover in the study area has been performed using Landsat satellite images (4, 5 and 8) for the years 1985, 1990, 1995, 2000, 2010, 2015 and 2017.
The false color images of the OLI sensor used were referred to the panchromatic band with 20 ground control points and observing the appropriate RMSe (0.28) of the ground. Then the images of MSS and TM sensors were referenced by image-to-image method with RMSe less than 0.5 ground. In this regard, the transfer potential modeling was performed with a learning procedure algorithm based on Multilayer Perceptron and prediction of changes for the best model was performed using Markov chain. Then it was used to evaluate the accuracy of modeling with Hit, Misses and False alarm statistics.
Discussion and conclusion
The results of the study of vegetation indices showed an improvement in the condition of cover in the study period. The results of the study of vegetation indices showed an improvement in the condition of cover in the study period. The study of land use also showed that with the current trend, the area of uncovered land will be reduced and the area of rangeland and forest cover will be increased. Therefore, the improvement of the coating conditions can be attributed to the application of protective operations.
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