Faculty of geomatics, Department of GIS

This research is aimed at developing a model for assessing land use compatibility in densely built-up urban areas. In this process, a new model was developed through the combination of a suite of existing methods and tools: geographical information system, Delphi methods and spatial decision support tools: namely multi-criteria evaluation analysis, analytical hierarchy process and ordered weighted average method. The developed model has the potential to calculate land use compatibility in both horizontal and vertical directions. Furthermore, the compatibility between the use of each floor in a building and its neighboring land uses can be evaluated. The method was tested in a built-up urban area located in Tehran, the capital city of Iran. The results show that the model is robust in clarifying different levels of physical compatibility between neighboring land uses. This paper describes the various steps and processes of developing the proposed land use compatibility evaluation model (CEM).

Developing e-land administration systems have proven to be crucial for realizing e-government. Land administration systems in different countries are developed through consideration of existing economical, societal and legal structures. E-land administration includes the coordination among various parts of land administration businesses, online customer services, private partnership services and internal workflows. A variety of organizations collaborate in land administration due to the diversity of activities in this ...

Corresponding Author: Dr. Gholam Reza Fallahi, National Cartographic Center, PO Box 13185-1684, Meraj St., Azadi Sq., Tehran, Iran Email: falahi-gh@ncc.org.ir 300 ... A Methodology Based on Ontology for Geo-Service Discovery ... 1 Gholam Reza Fallahi, 2 Mohammad ...

Classical and geostatistical methods have been used to create continuous surfaces from sampled data. A common geostatistical method is kriging, which provides an accurate estimation based on the existing spatial structure of sample points. However, kriging is sensitive to errors in the input data, the dispersion of the sample points, and the fit of the model to the variogram. The purpose of this research is to develop a new method to address the uncertainties resulting from the input data and choice of model in the kriging method. In our approach, the existing uncertainties in the input data are modeled by fuzzy computations, and the variogram variables are optimized by a genetic algorithm. To test this new hybrid method, sodium contamination values in the Zanjan aquifer were used. The results show a general improvement in accuracy compared with the ordinary kriging method. Consideration of all equations and values in fuzzy computations highlights the complexity of the computation. Herein, the integration problems experienced by other researchers when trying to use fuzzy kriging are resolved.

Predicting air pollution is an important prerequisite for estimating, monitoring and mapping unknown pollution values. We can use fuzzy spatial prediction techniques to determine pollution concentration areas in practical situations where our observations are imprecise and vague. Fuzzy membership kriging with a semi-statistical membership function is an example of this type of technique. The implementation of fuzzy membership kriging extracts semi-statistical membership functions from data, and applies these functions to an indicator kriging model. Such functions, which can be linear or nonlinear, transform fuzzy data into membership degrees and grades. Evolutionary genetic algorithms (GAs) can improve prediction efficiency and make it easier to choose an optimum membership function for air pollution applications. In this paper, we used a GA to determine the threshold parameters for a fuzzy membership kriging function based on preprocessed data from Tehran, Iran. We measured particulate matter with a mass median aerodynamic diameter of less than 10 lm (PM10) concentrations at 52 sample stations in Tehran to identify areas that are dangerous for human health. After we predicted the PM10 data, our results showed that GAs reduce the estimated error (3.74) compared to linear functions (8.94 and 12.29). This study indicates that using a GA for optimizing membership functions can get higher estimated accuracy than fuzzy membership kriging for modeling uncertainty in the prediction process of PM10 data.

The proposed methodology relies on the fuzzy nine-intersection matrix which is a generalization of the crisp four-intersection matrix for topological similarity computing. The similarity computation between 3D fuzzy matrix and 3D crisp nine-intersection matrix enables the decision variables to be derived. Decision variables, which are used for deducing and drawing conclusion, are consisted of semantic parts and quantifiers (type and strength of relations). Since these variables are dependent on the boundary directly, it is essential to present an efficient method for defining 3D fuzzy boundary. So, in this paper, we complete the information about how we can define fuzzy boundaries between two 3D phenomena and present a new procedure for simulation of 3D spatial topology in a deductive geographic information system (GIS). Therefore, a fuzzy knowledge-base system and an inference engine will be shown results for deduction in GIS environment.

Short time prediction is one of the most important factors in intelligence transportation system (ITS). In this research, the use of feed forward neural network for traffic time-series prediction is presented. In this paper, the traffic in one direction of the road segment is predicted. The input of the neural network is the time delay data exported from the road traffic data of Monroe city. The time delay data is used for training the network. For generating the time delay data, the traffic data related to the first 300 days of 2008 is used. The performance of the feed forward neural network model is validated using the real observation data of the 301st day.

One of the main factors of land use change (LUC) modeling is the land use intersection (LUI) or neighborhood effect, which is normally modeled using cellular automata (CA) concept. The effects of LUI over distance are represented in terms of CA transition rules. In this paper, a new model for LUI process is developed that makes use of expert knowledge to define the transition rules. In this model, the region of influence is defined using a new radial structure; the transition rules are described by expert knowledge and spatial metrics in the form of linguistic variables; and finally, the neighborhood effect is classified into three groups of compactness, dependency and incompatibility. The model is implemented and evaluated using the data of Borkhar and Meymeh township, in Esfahan, Iran, for the two periods of 1986-1998 and 1998-2005. The results show that the model and its related concept are performing rather well.

Service oriented architecture in a distributed computing environment, with loosely coupled geo-services is a new approach for using GIS services in environmental modeling. The messages exchanged must follow a set of standard protocols which support syntactic interoperability, but do not address application semantics. This article proposes a layer-based ontology with additional layers for describing geoservices, especially the measurement units used. The paper gives an ontology of measurements for describing the input and output of field-based geo-services and a core ontology of geo-services containing the domain concepts. An upper ontology adds new general concepts to an existing ontology in order to achieve an agreement between geoservice developers and environmental modelers. The layer-based structure is the building block for discovering geo-services that support semantic interoperability in GIS and environmental modeling.

Urban Land Use Planning (ULUP) requires the combination of large volumes of high quality and timely information and proper land use decision models. These are the key factors in applying spatial decision support system (SDSS) as a tool in supporting ULUP. Notwithstanding the huge abilities of GIS in analyzing spatial data, for many complex land use planning problems, current GIS are lacking mathematical modeling and simulation capabilities necessary in land use planning. A proper response to these shortcomings is the development of SDSS. However, many previously-used models/methods used for ULUP can not be easily modeled in and used by GIS and SDSS. Therefore, for using of GIS and SDSS unlimitedly and beneficially, a fresh look is necessary to develop new models/methods and procedures in the field of urban land use planning activities. This paper explains a developed GIS-based method for evaluation of different Land Use/Utilization Types (LUT) in built up parts of urban area. This method can be used for assessing different usages of urban lands and generating alternative land use plans using of GIS and DSS. It introduces a GIS-based decision support system to help urban land use planners in analyzing the suitability of current LUTs in built up urban area according to some quantitative and qualitative criteria. It can also be used to determine problems related to various LUTs in such built up urban area. In this method, we have tried to take a new viewpoint to some traditional concepts in urban planning such as Compatibility Matrix (each LUT in an area should not bring down the usability of other groups of land use types), Desirability Matrix (suitability of each site for its currently used LUT), Capacity Matrix (each LUT has a area of influence depend on it's activities) and Dependency Matrix (each LUT needs other special LUTs within or in its vicinity to be run and used by people properly and efficiency). In other words, we have integrated GIS with the related rules and criteria represented in the mentioned matrices to develop a SDSS for assessment of various urban LUTs.

Location-allocation is a combinatorial optimization problem, and is defined as Non deterministic Polynomial Hard (NP) hard optimization. Therefore, solution of such a problem should be shifted from exact to heuristic or Meta heuristic due to the complexity of the problem. Locating medical centers and allocating injuries of an earthquake to them has high importance in earthquake disaster management so that developing a proper method will reduce the time of relief operation and will consequently decrease the number of fatalities. This paper presents the development of a heuristic method based on two nested genetic algorithms to optimize this location allocation problem by using the abilities of Geographic Information System (GIS). In the proposed method, outer genetic algorithm is applied to the location part of the problem and inner genetic algorithm is used to optimize the resource allocation. The final outcome of implemented method includes the spatial location of new required medi...

Routing in a multimodal urban public transportation network, according to the user's preferences, can be considered as a multi-objective optimisation problem. Solving this problem is a complicated task due to the different and incompatible objective functions, various modes in the network, and the large size of the network. In this research, two optimisation algorithms are considered for solving this problem. The multi-colony and multi-pheromone Ant Colony Optimisation (ACO) algorithms are two different modes of the Multi-Objective ACO (MOACO) algorithm. Moreover, according to the acquired information, the algorithms implemented in the public transportation network of Tehran consist of four modes. In addition, three objective functions have been simultaneously considered as the problem's objectives. The algorithms are run with different initial parameters and afterwards, the results are compared and evaluated based on the different obtained routes and with the aid of the con...

Todays, stability of human life is threatened by a set of parameters. So sustainable urban development theory is introduced after the stability theory to protect the urban environment. In recent years, sustainable urban development gains a lot of attraction by different sciences and totally becomes a final target for urban development planners and managers to use resources properly and to establish a balanced relationship among human, community, and nature. Proper distribution of services for decreasing spatial inequalities, promoting the quality of living environment, and approaching an urban stability requires an analytical understanding of the present situation. Understanding the present situation is the first step for making a decision and planning effectively. This paper evaluates effective parameters affecting proper arrangement of land-uses using a descriptive-analytical method, to develop a conceptual framework for understanding of the present situation of urban land-uses, b...

This paper aims to provide a spatial and temporal analysis to prediction of monthly precipitation data which are measured at irregularly spaced synoptic stations at discrete time points. In the present study, the rainfall data were used which were observed at four stations over the Qara-Qum catchment, located in the northeast of Iran. Several models can be used to spatially and temporally predict the precipitation data. For temporal analysis, the wavelet transform with artificial neural network (WTANN) framework combines with the wavelet transform, and an artificial neural network (ANN) is used to analyze the nonstationary precipitation time-series. The time series of dew point, temperature, and wind speed are also considered as ancillary variables in temporal prediction. Furthermore, an artificial neural network model was used for comparing the results of the WTANN model. Therefore, four models were developed, including WTANN and ANN with and without ancillary data. Several statistical methods were used for comparing the results of the temporal analysis. It was evident that at three of the four stations, the WTANN models were more effective than the ANN models, and only at one station, the ANN model with ancillary data had better performance than the WTANN model without ancillary data. The values of correlation coefficient and RMSE for WTANN model with ancillary data for the validation period at Mashhad station which showed the best results were equal to 0.787 and 13.525 mm, respectively. Finally, an artificial neural network model was used as an alternative interpolating technique for spatial analysis.

Detection of homogeneous climate areas is a challenging issue, which can be affected by different criteria. One of the most prominent factors is choosing the time scale, which can lead to different spatial and temporal patterns. Total precipitation is a key factor in climatological studies, and studying its distribution is of utmost importance. The combination of principal components analysis and cluster analysis is used for homogeneous precipitation areas' detection. Hence, the spatial pattern of total precipitation was investigated in northwestern Iran during the past two decades (1991–2010) on seasonal and annual time scales. The results of clustering on each time scale were validated, and well-defined clusters were investigated and compared with each other. Two homogeneous sub-regions were recognized in spring, the best period for depicting homogeneous precipitation clusters at seasonal resolution. The annual pattern of precipitation delineated three clusters in the study re...

Traffic signal control plays a pivotal role in reducing traffic congestion. Traffic signals cannot be adequately controlled with conventional methods due to the high variations and complexity in traffic environments. In recent years, reinforcement learning (RL) has shown great potential for traffic signal control because of its high adaptability, flexibility, and scalability. However, designing RL-embedded traffic signal controllers (RLTSCs) for traffic systems with a high degree of realism is faced with several challenges, among others system disturbances and large state-action spaces are considered in this research. The contribution of the present work is founded on three features: (a) evaluating the robustness of different RLTSCs against system disturbances including incidents, jaywalking, and sensor noise, (b) handling a highdimensional state-action space by both employing different continuous state RL algorithms and reducing the state-action space in order to improve the performance and learning speed of the system, and (c) presenting a detailed empirical study of traffic signals control of downtown Tehran through seven RL algorithms: discrete state Q-learning(), SARSA(), actor-critic(), continuous state Q-learning(), SARSA(), actor-critic(), and residual actor-critic(). In this research, first a real-world microscopic traffic simulation of downtown Tehran is carried out, then four experiments are performed in order to find the best RLTSC with convincing robustness and strong performance. The results reveal that the RLTSC based on continuous state actor-critic() has the best performance. In addition, it is found that the best RLTSC leads to saving average travel time by 22% (at the presence of high system disturbances) when it is compared with an optimized fixed-time controller.

After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared ...

Sustainable development is regarded as a pivotal factor for smart urban planning. Transit-Oriented Development (TOD) is a well-known land use transportation integration (LUTI) planning method, which can fulfill sustainable development objectives. In this study, a new spatial index is developed to measure TOD levels in neighborhoods of Tehran, the capital of Iran. To develop the TOD index, several criteria and indicators are first computed using spatial analyses, before being aggregated using a fuzzy-analytic hierarchy process (fuzzy-AHP). The fuzzy-AHP method generates three types of factor maps: that are optimistic, pessimistic, and moderate. This process evaluates the sensitivity of the TOD index by determining the indicators' weights from various views, or perspectives. The results of this sensitivity analysis show the robustness of results from various views. Furthermore, in order to assess the efficiency of the proposed method, the moderate TOD-level map is compared with both the level of public transit services and trip attraction in neighborhoods. This comparison shows that the TOD map has an accuracy of 77 percent in urban modeling, which verifies the efficiency of the proposed method for measuring TOD.