Ali Mansourian

Evacuation Planning Using Multiobjective Evolutionary Optimization Approach

In an emergency situation, evacuation is conducted in order to displace people from a dangerous place to a safer place, and it usually needs to be done in a hurry. It is necessary to prepare evacuation plans in order to have a good response in an emergency situation. A central challenge in developing an evacuation plan is in determining the distribution of evacuees into the safe areas, that is, deciding where and from which road each evacuee should go. To achieve this aim, several objective functions should be brought into consideration and need to be satisfied simultaneously, though these objective functions may often conflict with each other. This paper aims to address the use of multiobjective evolutionary algorithms (MOEA) and the geographical information system (GIS) for evacuation planning. The paper proposes a three-step approach for evacuation planning. It explains that the last step, which corresponds to distribution of evacuees into the safe areas, is a spatial multiobjective optimization problem (MOP), because the objective functions and data required for solving the problem has a spatial component. To solve the MOP, two objective functions are defined, different algorithms for solving the problem are investigated, and the proper algorithm is selected. Finally, in the context of a case study project and based on the proposed approach and algorithm, evacuation planning is conducted in a GIS environment, and the results are tested. This paper is based on an ongoing research project in Iran.

Using SDI and web-based system to facilitate disaster management

Spatial data and related technologies have proven to be crucial for effective collaborative decision-making in disaster management. However, there are currently substantial problems with availability, access and usage of reliable, up-to-date and accurate data for disaster management. This is a very important aspect to disaster response as timely, up-to-date and accurate spatial data describing the current situation is paramount to successfully responding to an emergency. This includes information about available resources, access to roads and damaged areas, required resources and required disaster response operations that should be available and accessible for use in a short period of time. Any problem or delay in data collection, access, usage and dissemination has negative impacts on the quality of decision-making and hence the quality of disaster response. Therefore, it is necessary to utilize appropriate frameworks and technologies to resolve current spatial data problems for disaster management. This paper aims to address the role of Spatial Data Infrastructure (SDI) as a framework for the development of a web-based system as a tool for facilitating disaster management by resolving current problems with spatial data. It is argued that the design and implementation of an SDI model and consideration of SDI development factors and issues, together with development of a web-based GIS, can assist disaster management agencies to improve the quality of their decision-making and increase efficiency and effectiveness in all levels of disaster management activities. The paper is based on an ongoing research project on the development of an SDI conceptual model and a prototype web-based system which can facilitate sharing, access and usage of spatial data in disaster management, particularly disaster response.

Rational function optimization using genetic algorithms

In the absence of either satellite ephemeris information or camera model, rational functions are introduced by many investigators as mathematical model for image to ground coordinate system transformation. The dependency of this method on many ground control points (GCPs), numerical complexity, particularly terms selection, can be regarded as the most known disadvantages of rational functions. This paper presents a mathematical solution to overcome these problems. Genetic algorithms are used as an intelligent method for optimum rational function terms selection. The results from an experimental test carried out over a test field in Iran are presented as utilizing an IKONOS Geo image. Different numbers of GCPs are fed through a variety of genetic algorithms (GAs) with different control parameter settings. Some initial constraints are introduced to make the process stable and fast. The residual errors at independent check points proved that sub-pixel accuracies can be achieved even when only seven and five GCPs are used. GAs could select rational function terms in such a way that numerical problems are avoided without the need to normalize image and ground coordinates.

Surveying general prospects and challenges of GIS implementation in developing countries: a SWOT-AHP approach

We propose a combined method based on the strengths, weaknesses, opportunities and threats (SWOT) and analytic hierarchy process (AHP) to investigate the challenges and prospects of adopting geographic information systems (GIS) in developing countries. In this context, we identify, group, and analyse SWOT indicators in relation to the main GIS components: data, people, and technology. The relative significance of each SWOT indicator and its related SWOT groups in each GIS component is quantified. The method is then applied in a situation assessment of GIS adoption in the governmental organisations and strategic planning. The SWOT–AHP approach proves to be very useful in identifying and quantifying the relative significance of the major factors affecting GIS implementation, and effectively facilitates GIS strategic planning.

Spatial and Temporal Analysis of Fires Detected by MODIS Data in Northern Iran From 2001 to 2008

Fire, a natural disaster, has significant effects on ecosystems and plays a major role in deforestation, and it is a major source of trace gases, aerosols and carbon fluxes. Remote sensing is a valuable data source to investigate different phases of fire management. The Moderate Resolution Imaging Spectroradiometer (MODIS) has been designed to include specific characteristics for fire detection. It provides global coverage every 1 to 2 days. MODIS for forest fire monitoring has high detection accuracy, high radiometric resolution, moderate spatial resolution modes, and a high saturation level. Fires occur repeatedly in Iranian forests during the summer time. According to the Food and Agriculture Organization (FAO) reports, 0.06% of Irans forests burn every year. Fire season in the northern part of Iran is from May until the end of October. The results show that 86.21% of the fires detected by MODIS from 2001 to 2008 occurred in cropland, grass land and plain regions. Most of these fires occurred in the eastern regions of the Mazandaran Sea. A correlation of 0.76 exists between the fire frequency and the rainfall. Areas with precipitation lower than 1000 mm experienced 86.01% of the fires. Most of the fires occurred at elevations below 500 m from mean sea level (MSL). The fire frequency has a correlation of 0.58 with the average monthly Normalized Difference Vegetation Index (NDVI) values. Temporal analysis from 2001 to 2008 shows that most of the fires occurred in June.

Spatial Multi-Objective Optimization Approach for Land Use Allocation Using NSGA-II

Analysis and evaluation of land use patterns are of prime importance for natural resources management. Recent studies on land use allocation have been mainly based on linear programming optimization. Although these methods have the ability to solve multi-objective problems, spatial aspects of optimization are not considered when they are used for land use management. This study applied the non-dominated sorting genetic algorithm II (NSGA-II) to optimize land-use allocation in the Taleghan watershed, northwest of Karaj, Iran. The four land use classes of irrigated farming, dry farming, rangeland, and other uses were extracted from the ETM+ image. The objective functions of the proposed model were erosion, economic return, suitability, and compactness-compatibility. A novel crossover operator called exchange randomly block (ERB) was used to exchange information between individuals. Results showed that the optimization model can find a set of optimal land use combinations in accordance with the proposed conditions. For comparison purposes, land use allocation was also done using the combined goal attainment-multi-objective land allocation (GoA-MOLA) approach. The results showed that NSGA-II performance acceptably when compared to GoA-MOLA.

Polarimetric SAR feature selection using a genetic algorithm

One of the main applications of polarimetric synthetic aperture radar (POLSAR) images is terrain classification. In this study, an algorithm is presented to extract optimized features of POLSAR images that are required for classification. The proposed algorithm involves three main steps: (i) feature extraction using decomposition algorithms, including both coherent and incoherent decomposition algorithms; (ii) feature selection using a combination of a genetic algorithm (GA) and an artificial neural network (ANN); and (iii) image classification using the neural network. The algorithm is applied to a data set composed of different land cover elements, such as manmade objects, oceans, forests, and vegetation. The classification results obtained by the GA-based feature selection method exhibit the highest accuracy. The best features from the extracted features were identified and used in the classification based on the proposed algorithm.

A web-based spatial decision support system to enhance public participation in urban planning processes

Urban planning processes are complex and multidimensional and they require re-thinking of traditional approaches. One of the theories increasingly researched by urban planners is realisation of participatory planning to facilitate public participation. This approach, as an effective bottom-up method, should increase the capabilities of planners to solve urban problems by including the general public in the urban planning process. This paper proposes a framework for a Web-based spatial decision support system (WebGIS-SDSS) adjusted to a participatory urban planning approach. The framework, called Web-based participatory urban planning (WPUP), has been implemented in a case study. It offers a Web-based spatial forum to demonstrate how the proposed system can not only help citizens to share their idea regarding urban plans, but also help planners to implement applicable bottom-up and public participation decision-making theory in the urban planning process. WPUP supports the deliberative and analytic components of the collaborative spatial decision-making process. It facilitates inclusion of local knowledge in urban planning to complement traditional approaches. WPUP provides easy access to urban plans and decisions, improves communication among planners and public, involves citizens’ preferences on planning in a timely manner, and finally facilitates participatory planning approach.

Seismic human loss estimation for an earthquake disaster using neural network

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.

Investigating the system dynamics technique for the modeling and simulation of the development of spatial data infrastructures

Spatial data infrastructure (SDI) is a complex system for which huge investments are being made worldwide. These large-scale investments in the development of SDIs incontrovertibly require reliable design and planning that guarantee a successful outcome. One approach to deal with such an expectation is to model the development process of the SDI system over time. If the model can be translated into the computer-based environment to be used as a virtual world, then the real situation can also be simulated. Such a simulation will enable the SDI coordinators/managers to gain knowledge about the behavior of the system under different decisions and situations and eventually help them to better develop the SDI through the informed decision making. However, a limited number of tools and techniques are currently available in the SDI modeling history in terms of the modeling and simulation of such a complex system. The system dynamics technique based on systems theory is a method for modeling and managing the feedback systems that are complex, dynamic and nonlinear over time. This article addresses the applicability of the system dynamics technique for modeling and simulating the development process of SDIs. It is argued that the system dynamics technique is capable of modeling the interactions among the factors affecting the SDI, the feedback loops and the delays. It is also highlighted that an SDI model based on the system dynamics technique enables the SDI coordinators/managers to simulate the effect of different factors or decisions on various aspects of SDI and evaluate alternative decisions and/or policies prior to making any commitment.