فهرست مطالب سعید نادی

In this paper, we propose a quantitative indicator for measuring and ranking real estate from the perspective of the surrounding landscape by integrating the building information model (BIM) and the three-dimensional geospatial information system (3DGIS) based on the AHP method. The landscape is one of the qualitative variables which it's measuring and quantifying is a complex task. In previous studies, mainly, the Hedonic method and 3DGIS-based methods have been used to measure and evaluate the property landscape. The 3DGIS-based methods failed in describing building texture information and the exact position of the observer. Also, modeling and calculating important variables, such as the opinion of local experts, visible surface, vertical and horizontal field of view angles that significantly affect the validity of assessment results is a complex procedure and needs an effective method. For this reason, presenting a new method for scientifically measuring the value of a building's landscape is an attractive research issue. This paper presents a method that combines BIM, 3DGIS and AHP to model, measure and rank real estate with respect to landscape. In the proposed method, the position of the observer, the experts' opinion, the size and position of the windows of the building, field of view angles, diversity, quality and level of landscape are considered. In order to realistically estimate the landscape score, an ordered weighted average (OWA) operator is designed to provide the ability to estimate the landscape score in three modes: optimistic, normal and pessimistic. The performance of the proposed method is evaluated on two apartment units located in two buildings in the area of ​​Chitgar Lake in Tehran. The results of the evaluations indicate the well performance of the proposed formulation.

Land subsidence induced by soil consolidation is one of the natural hazards that occur gradually. This phenomenon has reached its critical state in most regions of Iran. Factors affecting subsidence are groundwater level depletion, land cover, soil type, elevation, slops gradient, rock bed depth, etc. The causes of subsidence should be investigated so that decisions could be based on the real characteristics of the region. Decrease in groundwater level is one of the most important factors that influences subsidence but due to the complexity of the relationship between subsidence and other factors, a direct linear relationship between groundwater level and subsidence cannot be considered. The study aimed to investigate the relationship between subsidence and groundwater changes through relationship between the two parameters in the period 2014 to 2018 in Isfahan. In this regard, the time series of radar interferometry and the time series of water levels of piezometric wells in the Isfahan were studied. The results show that in 12% of the wells, no correlation exists, in 9%, poor positive ignorable correlation, in 12%, fair positive correlation, in 48%, significant positive correlation, in 6%, poor negative ignorable correlation, in 4% fair negative correlation and in 9% there is a strong and negative significant correlation between the subsidence rate and the groundwater level. Correlation values show the complexity of the relationship between subsidence and water level depletion. Therefore, the relationship between subsidence and its causative factors requires more detailed studies and comprehensive models.

In recent years, air pollution has become one of the most important environmental challenges in large and industrial cities such as Tehran. High concentration of particulate matter with a diameter of less than 2.5 μm (PM2.5), which is known as the main cause of pollution in Tehran, is associated with irreversible effects on human health. Providing spatial-temporal model with high accuracy and speed for forecasting, is an effective way to protect public health against the increase of harmful air pollutants. The rapid growth of computing technologies and the availability of air quality data have provided researchers with the opportunity to provide sophisticated models in the context of machine learning, especially in deep learning to predict the concentrations of various air pollutants. In this study, with the aim of predicting PM2.5 concentrations at different time intervals, a new spatio-temporal deep learning model based on gated recurrent units (GRU) is presented which maintains and extracts temporal and spatial dependencies in the time series of air pollution datasets. The proposed model has been compared with support vector machine regression (SVR) and long-term memory (LSTM) methods as competitive approaches. The data used in this study include the hourly concentration of PM2.5 and meteorological parameters recorded by 13 air pollution monitoring stations and 3 synoptic meteorological stations in Tehran in the period of December, 2016 to February, 2019, respectively. The model presented in this paper with the RMSE of 7.97 μg/m3 and MAE of 5.35 μg/m3 has the best result for predicting air contamination compared to other methods. This model can determine 80% (R2=80) of PM2.5 concentration changes and predict contamination level. The proposed model also proves that it can be used effectively to predict and control air pollution by extracting temporal properties, simultaneous forecasting for all stations and considering spatial correlations.

Due to the adverse effects of pollutants on the environment and human health, the analysis of air quality data has an important role in protecting the environment and tackling air pollution problems. Missing data in time series, especially air pollution data, Cause a particular challenge to the analysis of these data, which show the importance of using methods known as imputation in order to deal with this phenomenon. Missing values reduce the volume of data, change time patterns in data and make inaccurate conclusions in data analysis. In this study, in order to estimate the missing values in time series data of PM_2.5 pollutant concentration from 12 contamination stations in Tehran, a hybrid algorithm based on regression algorithm considering spatial and temporal similarities and dependence by dynamic time wrapping algorithm is presented. Data with missing values with a pattern similar to the original data were simulated in the interval of 10, 15 and 20% missing in data, with the aim of evaluating the performance of the single and multiple imputation models. Then the proposed method in combination with different multiple imputation methods such as classification and regression tree, random sample and predictive mean matching, have been implemented and results have been compared with single imputation methods. Implementation results indicated the superiority of the proposed method combined with regression tree and linear interpolation compared to other methods of multiple and single imputation

All transportation specialists agree to achieve the sustainable transports pattern in cities. Public transportation plays a crucial role in sustainable transport. It is necessary to do some proceeding for improvements of management and increase productivity, in order to increase its share out of urban transport. In this context, the most important step in changing of bus transit systems quality is modifying or redefining of the lines route. This process is known as strategic step for long-term program management. In this research, 9 effective parameters has been selected to present a model for Determining the optimal route of bus lines on the basic of data Khomeini-shahr of Isfahan which its goal is increasing coverage of bus network in cities, as well as reducing the number of parallel lines in streets. These parameters include Population covered every street, passengers, Type of roads, Level of service, Speed, Facilities of each station, Available density, Land use and length of each link.The selected parameters are weighted according to the impact of the problem, by Analytical Network Process and number of expert ideas, Afterword, the obtained weights are entered in the Network Analysis extension to do New Route operation. Consequently, a new network is established which changes 20 lines of the existing network, increases network coverage more than 18000 persons and declines 22 km overlap of the lines.

Urbanemergency vehicles, due tosensitivity oftheir mission, are alwayslooking forthe shortest timeto reach the destination. In great cities, in addition todistance, several factors and parameters, with respect to complexity and extent of thetransport andtraffic, are influencing on time of arrival of an emergency vehicle, some of the parameters are qualitative or quantitative, dynamic or static. In this paper, the modern approach used is based on composing conflation models, Gamma quantification methods, travel time prediction formulas and metaheuristic algorithms in order to find most optimal route. In this paper, first we have tried to introduce all the calculated, available, qualitative and quantitative, affecting factors related to emergency routing, thenwith converting qualitative parameters to quantitative one, normalize each parameter by the maximum approach and conflate them in such a way that thepriority and impact of each parameteris determined to find the optimal route.In order to calculating the priority and impact of factors, the Gamma test method, as a data derived method is selected. The procedure implemented by use of road network and traffic volume data from two regions of Tehran. Based on this approach, the considered weights for each following criterion of degree of difficulty including quality, width, slope, category and route directness are 0.331, 0.286, 0.188, 0.172 and 0.020, respectively. Finally, genetic meta-heuristic algorithm is used to select the optimal route and the results compared with common Dijkstra routing algorithm. The length of the selected route by GA is about 130 meters in one time and about 300 meter in the other time more than the selected one by Dijkstra algorithm. Based on the implemented comparison, the represented approach in this paper had a considerable superiority than the simple current methods.

Recent developments in open source digital elevation models (DEMs) increase their applications in different disciplines. The lack of proper information about the accuracy of these models in regions with different topographic characteristics, make their analysis unreliable. Most estimates of accuracy, e.g., mean, standard deviation and RMSE depend on an implicit assumption that the errors comprise a random sample from a normal distribution. But, analytic data often depart from that assumption. Therefore, this paper first study statistical characteristics of SRTM and GDEM open source global DEMs. Then we define a confidence interval for RMSE which make it possible to investigate the appropriateness of reference points. Afterwards, measures for accuracy assessment of DEMs based on robust methods in L1 norm and Huber's method as well as conventional methods in L2 norm are discussed. In order to consider different topographic characteristics we compare SRTM and GDEM based on the defined measures in Urban, Flat, Hill and Mountain regions. Finally, we studied how the errors propagate into slope and aspect maps. It was found that although GDEM has better resolution, but SRTM performs better based on the defined measures in all the regions. It is also illustrated that the accuracy of both models in urban are better than other regions.

Time and space are main parts of any phenomena in the real world. In spatial Information Systems (GIS) modeling processes are the first step in analyzing phenomena. Therefore including temporal aspects as well as spatial capabilities in GIS is necessary. One of the most important requirements in spatio-temporal modeling of phenomena in GIS is the ability to investigate their spatial and temporal topological relationships. In this paper we first explain different aspects of time as well as space and then propose a novel approach to extract a full list of temporal and also spatio-temporal topological relationships. Time as a distinct part of any phenomena can be considered to be zero or one dimensional. A zero dimensional temporal space is referred to a phenomena with start and end time occurrence in one chronon which is named event. A one dimensional temporal space is referred to a phenomena with a duration of existence and is named state. Accordingly, different spatial dimensions are point, line, area and volume. The proposed approach in this paper extracts all the possible relationships between different temporal aspects. The approach is based on a Boolean comparison matrix with two rows and four columns. Each cell of the matrix is a comparison between start and end of one temporal phenomena and start and end of the other which results true or false. This matrix leads to 28=256 relations which all of them are not logically acceptable. Therefore we explain how to find unacceptable relations. Removing these relations we find 13 relations between two temporal states, 6 relations between a temporal state and a temporal event and finally 3 relations between two temporal events. Furthermore, in order to conflate spatial and temporal topological relationships two strategies are proposed. In the first one, we used logical AND and OR operators and in the second strategy we consider the time to be an orthogonal dimension added to 2D spatial space. Each of these strategies leads to a comprehensive list of spatio-temporal relationships. Finally in order to illustrate the applicability of the extracted relationships, we implement them in a land information management system. The system is used to response spatial, temporal and spatio-temporal queries regarding historical situation of any parcels. As an example for spatial topological queries, the system is able to find parcels which are overlap with newly designed green space. Similarly the system can for example find land parcels which their duration of existence has same start point in time. This is a "start" temporal topological relationships. Regarding spatio-temporal relationship, the system can for example find land parcels which are coincide with a land parcel and are also neighbor to it.

Participation is the lifeblood of democracy. Indeed, the health and satisfaction of a democracy is often seen in its level of turnout. Therefore, understanding of the reason of voting by people, and stimulating factors of political participation, can be helpful for decision making in the political, economic, and social spheres. This study aims to investigate the determinants of voter turnout in Iran. In this study, the analysis of the conventional regression and spatial econometrics have been used to identify effective factors on voter turnout of the various provinces in presidential elections in 2013. The results indicate that among the various factors, unemployment and political- secure factors have had a negative impact, and the amount of the reformist candidates’ votes has had a positive effect on voter turnout. Spatial analysis also shows that there is no significant dependence between rates of voters’ participation.

In this study، the recovery of casing soil consumed in the white button mushroom was investigated. Casing soil is used as a layer on compost (culture medium) to a diameter of 3-5to stimulate fruiting on the white button mushroom (Agaricus bisporus). The expense od casing soil is about 30 percent of production costs. In the first step of this experiment، in order to separate casing soil from compost easily (at the end of production period)، by distribution of a mesh with pores of 5 mm between compost and casing soil in the casing step، which we could separate and easily collect casing soil at the end. The most important altering factor between chemical and physical properties of recycling casing soil، is EC that was reduced by leaching process. An experiment was conducted based on split plot design with two factors and three replications. The main plots were treated with two levels of with and without a plastic mesh. Percentages of recycled soils in combination with fresh casing soil applied in sub plots. Subplot included 100%، 75%، 50%، 25% and 0% of recycled soil. Three traits included fruit yield، fruit average weight and fruit number were analyzed using SAS software. None of the traits showed significant differences. No significant difference was obsereved between with and without plastic mesh. Further more no difference was observed between different percentages of recycled soil mixed with fresh peat. It is thus concluded that using recycled casing soil in production of the white button mushroom is possible and profitable.