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Residential segregation as a known consequence of rapid urbanization in developing countries is a complicated socio-economic phenomenon. Quantified description, analysis, and prediction of urban dynamics as a complex system have always been challenging issues. The recent intensive developments of multi-agent simulations as a solution to these problems still lack enough real-world examples. Our purpose in this research is to develop a spatially explicit model of residential segregation in urban space which accounts for a specific city's infrastructure. The proposed agent-based simulation of the residential dynamics of Tehran over a 20-year period between 1996 and 2016 is based on the GIS datasets provided by Tehran Municipality and the Statistical Center of Iran.This is the first effort at presenting an agent-based model for Tehran's residential segregation. We revised the "Schelling" segregation model in an attempt to customize it and arrive at an acceptable fit for Tehran. In addition to Schelling's parameters, which comprise purely social factors, we identified several important socio-economic and spatial-environmental criteria, in turn categorizing them based on the AHP method. A certain number of the parameters like "neighborhood prestige" specifically belong to Tehran and are suggested for the first time. The proposed expert-based model was implemented in Netlogo. Validation of the resulting pattern using the Kappa indicator showed that the model simulated Tehran's segregation pattern at a rate higher than 62%. [ABSTRACT FROM AUTHOR]

In the last twenty years, destructive wildfires have affected the environment to the tune of billions of dollars. An accurate model is crucial for predicting the spreading of wildfires in a variety of conditions. In this study, a multi-kernel convolution neural network (CNN) deep learning model was proposed based on elevation, wind direction, and speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), and energy release component to predict wildfire spread across the United States. Using multi-kernel CNN, it is possible to predict whether a pixel will be on fire at a future time. Compared to the model presented by other authors, the multi-kernel CNN model achieved high accuracy and F1 score. In comparison with CNNs without a multi-kernel mechanism and fixed kernel size, the proposed model predicted more accurate results based on the test data set. The multi-kernel CNN model reached an overall accuracy of 98.6 and F1 score of 70.97 on test data. [ABSTRACT FROM AUTHOR]

The rainfall-runoff process is one of the central aspects of hydrology. In the rainfall-runoff process, the fast accumulation of rainwater in the catchments and rivers may lead to a flood disaster. To prevent and manage such disasters, it is essential to model and simulate the runoff process. The cellular automata (CA) is a computational model that can represent and simulate complex and dynamic natural phenomena. The main objective of this research is to develop a CA model of the rainfall-runoff process and to use it for simulating and representation of the runoff in the Babol catchment. The study is carried out in two main steps. First, several data manipulation and analyses in ArcGIS 10.3 software are used to prepare the input data of the CA model. In the second step, the CA model is developed and implemented in the NetLogo 6.0.4 platform. The total amount of runoff in each cell during the year 2016 was calculated and presented as the output of the model. A map of the total generated runoff can be an important tool for flood management decision-makers and it can also be used for managing the catchment and assessing the water resources in the study area. [ABSTRACT FROM AUTHOR]

Vehicle Routing Problem is one of the classic problems in GIS (Geospatial Information System) which had been studied for long times. An answer can be accepted as a good solution if it would be able to optimize the total length of the route or decrease the number of vehicles. A VRP defines finding the optimum route for some vehicles that serve to some customers and return to the service center. This problem is economically important because the cost and the time of serving to costumers are related to optimization of the problem's answer. Furthermore, there are many problems like BUS management, Post pickup and delivery system and other servicing systems, which are technically similar to VRP. The aim of these problems is finding a composition of optimum routes between server and costumers. In addition, as the cost is related to time, finding shortest path means decreasing cost serving and decreasing time. In this article, a hybrid model using Artificial Bee Colony and Genetic Algorithm is proposed to solve VRP. In the first step, Artificial Bee Colony has been used to find a solution for five vehicles. The scout and the onlooker bees produced in 8 modes by two methods including the nearest neighborhood and the wide neighborhood. In the second step, the Genetic Algorithm helps to optimize the solutions. The results show that the production of the scout bees is the most effective factor in the answers to the problem and helps greatly converging the answers as soon as possible. [ABSTRACT FROM AUTHOR]

The rainfall-runoff process is one of the central aspects of hydrology. In the rainfall-runoff process, the fast accumulation of rainwater in the catchments and rivers may lead to a flood disaster. To prevent and manage such disasters, it is essential to model and simulate the runoff process. The cellular automata (CA) is a computational model that can represent and simulate complex and dynamic natural phenomena. The main objective of this research is to develop a CA model of the rainfall-runoff process and to use it for simulating and representation of the runoff in the Babol catchment. The study is carried out in two main steps. First, several data manipulation and analyses in ArcGIS 10.3 software are used to prepare the input data of the CA model. In the second step, the CA model is developed and implemented in the NetLogo 6.0.4 platform. The total amount of runoff in each cell during the year 2016 was calculated and presented as the output of the model. A map of the total generated runoff can be an important tool for flood management decision-makers and it can also be used for managing the catchment and assessing the water resources in the study area. [ABSTRACT FROM AUTHOR]

Vehicle Routing Problem is one of the classic problems in GIS (Geospatial Information System) which had been studied for long times. An answer can be accepted as a good solution if it would be able to optimize the total length of the route or decrease the number of vehicles. A VRP defines finding the optimum route for some vehicles that serve to some customers and return to the service center. This problem is economically important because the cost and the time of serving to costumers are related to optimization of the problem's answer. Furthermore, there are many problems like BUS management, Post pickup and delivery system and other servicing systems, which are technically similar to VRP. The aim of these problems is finding a composition of optimum routes between server and costumers. In addition, as the cost is related to time, finding shortest path means decreasing cost serving and decreasing time. In this article, a hybrid model using Artificial Bee Colony and Genetic Algorithm is proposed to solve VRP. In the first step, Artificial Bee Colony has been used to find a solution for five vehicles. The scout and the onlooker bees produced in 8 modes by two methods including the nearest neighborhood and the wide neighborhood. In the second step, the Genetic Algorithm helps to optimize the solutions. The results show that the production of the scout bees is the most effective factor in the answers to the problem and helps greatly converging the answers as soon as possible. [ABSTRACT FROM AUTHOR]

The complex, dynamic and non-linear characteristics of land use change (LUC) modelling necessitate the development of advanced models and methods. In this research, a novel and stepwise approach is developed for modelling integrated LUC at the regional level based on land suitability, land demand and land use conversion rules. The aim of this model is to use different models (i.e. geographic information systems, remote sensing, fuzzy logic, cellular automata and multi-criteria decision analysis) in a logical way to improve model development, model evaluation and prediction of LUC. The proposed model was implemented in ‘Borkhar and Meymeh’ township, Iran, for two periods (1986–1998 and 1998–2005). Results for producer accuracy, user accuracy, overall accuracy and figure of merit were 0.345, 0.357, 0.351, 0.234 respectively in the first period (1986–1998) and 0.302, 0.287, 0.0.294, 0.202 respectively in the second period (1998–2005). Our results suggest that the developed model is accurate, when compared with the results of existing models. [ABSTRACT FROM AUTHOR]

Nowadays traffic data is obtained from multiple sources including GPS, Video Vehicle Detectors (VVD), Automatic Number Plate Recognition (ANPR), Floating Car Data (FCD), VANETs, etc. All such data can be used for route finding. This paper proposes a model for finding the optimum route based on the integration of traffic data from different sources. Ant Colony Optimization is applied in this paper because the concept of this method (movement of ants in a network) is similar to urban road network and movements of cars. The results indicate that this model is capable of incorporating data from different sources, which may even be inconsistent. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network's capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were used to train this neural network. The final results demonstrate that this neural network model can reveal much more accurate estimation of fatalities and injuries for different earthquakes in Iran and it can provide the necessary information required to develop realistic mitigation policies, especially in rescue operation. [ABSTRACT FROM AUTHOR]

The majority of spatial data users are inexperienced in spatial information science. This group of users cannot analyze spatial data and produce the required information. Therefore, the availability of mechanisms and applications for automatic and interoperable spatial data processing and information extraction is necessary. Generalization is one of the main geoprocesses, which is widely used. The current generalization algorithms using in web-based processes are controlled by users. Due to the importance of road network maps, in this study we propose a new scale-driven algorithm, which extracts pattern information of road networks and by considering geometrical, topological, semantic, statistical and structural characteristics of road networks, controls generalization process automatically. The algorithm's operation is similar to a set of model and cartographic generalization operators. A prototype service-oriented application is developed as well. The application consists of three types of services. This classification of services improves flexibility and semantic interoperability of the application. In addition, OGC¹ standards (WPS² and WFS³) are utilized to establish syntactic interoperability. We have used two methods of service chaining to coordinate services. The paper explains how various chains of the services satisfy requirements of various users using a case study. [ABSTRACT FROM AUTHOR]