The International Journal of Humanities
Volume:20 Issue: 2, 2013

An upward trend in the divorce rate in Iran in recent years has attracted officials، researchers and sociologists towards investigating causes and factors contributing such a social menace. Based on the statistics published by the Statistics Center of Iran (SCI)، the divorce rate has gone up from 1. 5 in 1000 cases in 1996 to 2. 3 in 1999 and 3. 1 in 2006. Results of previous studies show that factors such as age and educational differences between husband and wife، women’s employment، addiction and lack of moral principles have been the most important causes of the divorce. This study، however، focusing on the socio-economic status of the divorcee in Iran، picks up a different view from that of other studies conducted in this field. This article also tries to identify the relationships between the rules applicable to personal and employment variables among divorced people using exploratory spatial data analysis (ESDA) techniques. The sample data used in this study include 6400 divorcee from the total divorced population (of 392075) in the county according to the 2006 census; those who have declared themselves without marriage partner due to divorce. The sample includes both male and female. Results show that the main characteristics of divorced women were their employment and level of education which were statistically significant in metropolitan regions where there is a rise in the employment and education level of women. On the contrary، low education، unemployment، and place of work have been significant factors among divorced men.

Expansion of urban area is a well-known phenomenon in developing countries with population growth and the migration from villages to cities being two major factors. Those factors reduce the influence of efforts to limit the cities boundaries. Thus، spatial planners always look for the models that simulate the expansion of urban land-uses، and enable them to prevent unbalanced expansions of cities، and guide the developments to the desired areas. Several models have been developed and evaluated for simulating urban land-use expansions. Although these models are numerous، most of them have focused to simulate urban land-use expansions in sub-urban areas. The regional models that cover wider area are equally important. In this study، a new agent-based model has been developed and implemented to simulate urban land-use expansion in Qazvin and Alborz regions of Qazvin province، which cover 1620 square kilometres. In this model، land-use developers have been treated as computer agents that move in the landscape explicitly، and assess the state of parcels for development. The environment of the model is raster. The agents are categorized based on two scenarios. In the first scenario، all agents are of similar category and in the second scenario the agents are divided into five categories with different objectives. Then، the results of the two scenarios are compared. Due to the spatial essence of the problem، Geographical Information Systems (GIS) were used to prepare the environment of agents’ movement and search، and to aggregate and analyze the results. To evaluate the model، data of year 2005 were used as the input and data of year 2010 were used for checking the results. By calibrating the parameters، the most desired configuration of the model was found in the second scenario، since the results were close to the reality as the Kappa index raised up to 78. 17 percent. These results showed that the precision of the model to simulate land-use developments are of considerable quality. Thus، the model is able to detect the area that faced rapid urban expansions. Moreover، a comparison between the results of the two scenarios revealed that dividing the agents into categories with different aims and parameters will improve the outcome of the model. However، it is vitally important to determine the number of the agents in each category as well as their parameters precisely.

This study analyzes wind energy potentials of Zanjan province which located in northwest of Iran. Renewable energy plans are not fully environmentally safe and different renewable energy plans have different environmental impacts. Therefore، site selection is an important issue in the wind turbine installation and therefore، selecting an appropriate wind turbine site requires consideration of multiple alternative solution and evaluation criteria because of the system complexities. In this paper، using AHP and fuzzy TOPSIS techniques in conjunction with GIS، wind turbine potentials of the study area are evaluated. Criteria weights are obtained from pairwise comparison of identified criteria and after fuzzification of both criteria weights and criteria map layers using triangular fuzzy numbers، fuzzy TOPSIS technique is utilized to integrate and rank more suitable alternatives for the wind turbine installation. The results shows the ability of multi-criteria methods to evaluate suitable sites in geographic areas on one side and good potentials sites of Zanjan province to establish new energy plans on the other.

Total Perceptible Water (TPW) is an important parameter in climatology and weather forecasting and is directly related to any climate process. There are three approaches to estimate this parameter i. e. using radiosonde، using GPS and calculating from satellite images where the first two are localized and the last one can give an instant view of TPW in a vast region. The algorithm used for the TPW calculation from MODIS images is related to the ratio of the reflectance in a water vapor absorbing channel and the reflectance in a non-absorbing channel. Due to strong horizontal variation in the surface reflectance in non-absorbing channels، the retrieved TPW varies strongly from one pixel to its neighboring pixels while it is believed that the horizontal gradient of TPW is very weak. To solve this problem، a damping coefficient was added to the non-absorbing channel reflectance. It is found that this coefficient differs for different surface covers. The current work presents a procedure for calculating these coefficients. The results of a comparison between modified TPW and those extracted from GPS data showed a R2 of 0. 81 whilst this was about 0. 67 for non-modified MODIS TPW.

Geographic information and analysis provide a wide range of data and techniques to monitor and manage natural resources. As an important case، in arid and semi-arid areas، water management is critical for both local governance and citizens. As a result، the estimation of water potential brought by snowmelt runoff and rainfalls seems to be very useful and important for these areas. Hydrological modeling needs vast knowledge about integrating all relating parameters. In this work، different data sources including the remote sensing observations، meteorological and geological data are integrated to supply spatially detailed inputs for Snowmelt Runoff Modeling in a watershed، located in Simin-Dasht basin in the northeast of Tehran، Iran. Because of high temporal frequency and suitable spatial coverage، MODIS optical images have been chosen to map snow cover. The MODIS 8-day snow map product with spatial resolution of 500m (MOD10A2. 5) is used to compute the snow cover area. In addition، during the snowmelt period in 2006-2007، archived meteorological and geological data are used to provide snow runoff modeling (SRM) parameters and variables. Also Landsat ETM+ images with better spatial resolution (30m) and less temporal coverage (16 days) are used in 2007 snowmelt period to compare the model accuracy with same conditions. Evaluation of the runoff outputs in both of models reveals good agreement with real data that prove SRM capability in modeling basin’s daily and weekly runoff. Model accuracy shows better satisfactory of snow runoff modeling results within snow cover area derived from Landsat ETM+ data and MODIS snow product was less accurate in modeling. Although using MODIS model accuracy was less، but still it is recommended due to less further process and providing better temporal coverage during snowfall and snowmelt season. Future works in this criterion could be concentrated on SRM forecast improvement using fusion with other measurements or combining physical models.

Hyperspectral sensors، by accurate sampling of object reflectance into numerous narrow spectral bands، can provide valuable information to identify different land-cover classes. Nevertheless، classification of these data has some problems. In particular، one of the most well-known of them is not having adequate training data for learning of classifiers. One possible solution to this problem is the use of unsupervised classification such as Kernel based Fuzzy C-Means (KFCM). KFCM is a kernelized version of FCM algorithm، which usually، has better performance. However، in case of hyperspectral data، accuracy of the KFCM decreases because of high dimensionality of data and its kernel parameter. In this paper، the objective is to use the KFCM clustering and optimize it based on data dimensionality and kernel parameter. To optimize this algorithm with respect to the kernel parameter and data dimensionality، particle swarm optimization method (PSO) is introduced. In other words، PSO is a powerful optimization tool inspired from bird’s behavior، which can find global optimum. In this study، two new methods are defined to optimize KFCM with respect to kernel parameter and data dimensionality. The results show that the proposed methods have a better performance than the KFCM.