En uygun barınma alanlarının modeli için duyarlılık analizi

GIS (Geographical Information Systems) is one of many information technologies that have transformed the ways engineers and planners conduct research and contribute to society. In the past two decades, these information technologies have had tremendous effects on research techniques specific to geography, as well as on the general ways in which scientists and scholars communicate and collaborate. A spatial decision support system (SDSS) is an interactive, computer-based system designed to assist in decision making while solving a semi-structured spatial problem. It is designed to assist the spatial planner with guidance in making land use decisions. A system which models decisions could be used to help identify the most effective decision path (T Erden & Coşkun, 2010). Decision making on alternatives for risk reduction planning starts with an intelligence phase for recognition of the decision problems and identifying the objectives. Development of the alternatives and assigning the variable by decision makers to each alternative is employed to the design phase. The final phase evaluates the optimal choice by comparing the alternatives, defining indicators, assigning a weight to each and ranking them. This process is referred to as Multi‐Criteria Decision Making analysis (MCDM), Multi‐Criteria Evaluation (MCE) or Multi‐Criteria Analysis (MCA). MCDM is performed by choice and prioritization and is defined based on Alternative, Value, Criteria and the Weights on the Criteria (Turan Erden & COKSUN, 2007). Decision makers historically have indicated that inaccessibility of required geographic data and difficulties in synthesizing various recommendations are primary obstacles to spatial problem solving. Studies have shown that the quality of decisions can be improved if these obstacles are lessened or removed through an integrated systems approach, such as a spatial decision support system (SDSS). In addition, multi criteria decision making (MCDM) and a wide range of related methodologies offer a variety of techniques and practices to uncover and integrate decision makers' preferences in order to solve "real-world" GIS-based planning and management problems. However, because of conceptual difficulties involved in formulating and solving spatial decision problems, researchers have developed multi criteria-spatial decision support systems (MC-SDSS). Sensitivity analysis, as it is applied to risk assessment, is any systematic, common sense technique used to understand how risk estimates and, in particular, risk-based decisions are dependent on variability and uncertainty in the factors contributing to risk. In short, sensitivity analysis identifies what is "driving" the risk estimates. It is used in both point estimate and probabilistic approaches to identify and rank important sources of variability as well as important sources of uncertainty. The quantitative information provided by sensitivity analysis is important for guiding the complexity of the analysis and communicating important results (C. M. Z. Erden T., 2010). In this study, sensitivity analysis has applied to Istanbul optimum shelter location map parameters which are respectively Infrastructure (AL), Accessibility (ER), Hazards (TH), Topography (TO), Capacity (KA), Land Use (AK). Their weights were calculated with AHP that determined weights as AL (17.3%), ER (20.7%), TH (31.3%), TO (9.5%), KA (15.5%) and AK (5.7%). Sensitivity analysis applied to all criteria with only one criterion weight range changes between 0% to 100% and other criteria changes depend on this criterion. Also sensitivity analysis has applied to Sub-Criteria maps parameters which are by the weights as follows, Earthquake Hazard (DP - 7.4%), Tsunami Hazard (TS – 7.4%), Land Slide Hazard (HY – 13.2%), Flooding Hazard (SB – 13.2%), Chemical Hazard (KM – 22.7%), Fire Hazard (YN – 36.1%), GSM Base Stations (IL – 6.3%), Drinking Water (SU – 23.3), Waste Water (KN – 48.9), Electricity Lines (EL – 15.2%), Gas Lines (GZ – 6.3%), Roads and Highways (KR – 54.2%), Marine Ports (DN – 8.5%), Heliports (HV – 14%), Railways (DM – 23.3%). Turkey is among the countries that are especially vulnerable to natural disasters. Throughout the history, many disasters occurred in the geography where turkey area is located and earthquakes are the most destructive type of disaster that occurs in Turkey. Scientific studies show that Istanbul may face a big scale earthquake in near future. The main axis of these studies is to assess the one of the most important issues is the requirement of temporary shelter for the victims. Establishment of temporary shelters are most widely used method for protecting or sustaining the lives of the victims who may have suffered from disasters, Thus the correct locations for these areas will be determined before earthquake and it will be available for the decision makers to develop strategies to enhance and rehabilitate these areas. The system will also be efficient in post-earthquake situation for evaluating the selected sites. Moreover, it will be available to re-evaluate the efficiency of the shelter locations based on the new conditions that arise because of the earthquake. Integration of GIS with AHP decision-making process, with GIS visualization and analysis that combines the functions and decision-making processes by integrating action is improving the sensitivity analysis. Model parameters has chosen by decision makers thanks to the sensitivity analysis that may follow the general effects changes of the model in terms of productivity and quality will be increased in this way. In this context, GIS, disaster management and spatial decision making methods along with sensitivity analysis followed by an operation, decision-makers will follow more meaningful path in their work, decisions and practices to be applied that will minimize such losses life and financial. By using the sensitivity analysis results decision makers will be allowed to update the criteria simultaneously and new locations based on these optimum conditions will be assessed.