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This study includes the properties and installation stages of the microseismic network planned to be installed in a geothermal field in Turkey, which is currently producing electricity, in order to monitor the induced seismicity that will occur due to production and re-injection wells. The results of how the optimum microseismic monitoring network should be installed, the installation studies and the noise analysis of the station locations are given for this and similar geothermal fields. 7 borehole type seismometers were placed in wells drilled 65 meters deep and microseismic records of the field were obtained. The leading field equipment is SARA-SS10BHV three-component (3C) borehole type short-period velocity seismometers, and the analog data were digitized with 24-bit SL06 brand recorders. In data analysis, high frequency, low energy and small amplitude microseismic events were dealt within passive seismic records. Therefore, noise analysis is the most important step of the network installation. In the noise analysis studies, the background noise at the locations where the microseismic network stations will be deployed was determined by measurements from both on the surface and through the well, and then the results were compared with each other. According to the results, it has been determined that the noise level in the well is very low compared to the surface and this is at a suitable level for microseismic monitoring networks. [ABSTRACT FROM AUTHOR]

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This study differs from previous similar studies in three main directions. Firstly, the location of the most suitable facilities that can provide regional service were determined by applying clustering method. Thus, not only one airport facility in a single location serving across the country was determined, which separates this research from the others. Secondly, in the previous studies, Istanbul Airport was not taken into account because it has not been put into service. Therefore, this study takes that particular airport into consideration. Thirdly, optimum solutions for such new situation were produced with more up-to-date data and using a modern optimization method, the tree seed algorithm. As a result of the analyzes made based on traffic data, traffic-based weight coefficients and location data from which transportation distances were obtained, a total of sixteen ideal airports were recommended that can provide service to their regions, especially during emergencies or after any disaster. In accordance with the suggestions of this research, it is expected that progress will be made in minimizing the loss of time and expense, and especially minimizing the loss of life thanks to the services which can be provided by air transportation in any emergency.

The textile sector has an important role in the economic development process with its employment opportunities, the added value it creates in the production process and its share in international trade. Investments in this area and production in Turkey dates back to the Ottoman period. Today, the textile industry is one of Turkey's leading sectors. In which regions the textile sector has a concentration potential and in which regions the concentration is concentrated is an important issue. In this context, the study aims to reveal the spatial pattern of the 13 coded textile sector according to Nace 2 code and Level-2 regions. For this purpose, TurkStat 2009 and 2015 number of local units, number of employees and turnover indicators are taken as basis. The obtained data were analyzed with the Location Quotient technique which is one of the most preferred techniques in sectoral concentration analysis. Consequently, the number of local units in Turkey, employment and turnover index in industry manufacturing 13 coded textile products in all three parameters according to TR41- (Bursa, Eskişehir, Bilecik) and TR32- (Aydın, Denizli, Muğla). The results of this study will constitute the basis for the regional plans to be prepared; it also guides investors. [ABSTRACT FROM AUTHOR]

For emergency services such as ambulance systems and fire departments, location selection plays a critical role due to the direct impact of these services on human lives. Timeliness plays a primary role in location selection decision of fire stations for large metropolitan cities such as Istanbul with increasing population with a high level of congestion coupled with an imminent earthquake risk. This study is based on a set-covering model for locating new fire stations, which target to serve each area at most in five minutes and improve their coverage area to 100% for Istanbul Municipality Fire Department. Accordingly, a set-covering model is built and solved using the data retrieved from geographical information systems. Finally the change in service level with proposed fire station locations is investigated and further suggestions are provided. [ABSTRACT FROM AUTHOR]

In the early 1980s Geographical Information Systems (GIS) software emerged as a new information processing technology offering unique capabilities of automating, managing, and analysing a variety of spatial data. Many applications of GIS developed over the last decade provided information necessary for the decision-making in diverse areas including natural resource management, regional planning, and disaster management. Two perspectives on developing better decision support capabilities of GIS can be identified, one based on analytical problem solving as a centrepiece of Spatial Decision Support Systems (SDSS) and another based on integration of GIS and specialized analytical models. According to first perspective, SDSS should offer modelling, optimization, and simulation functions required to generate, evaluate, recommend, and test the sensitivity or problem solution strategies. These capabilities are essential to solving semi-structured spatial decision-making problems. The second perspective on improving the decision support capabilities focuses on the expansion of GIS descriptive, prescriptive, and predictive capabilities by integrating GIS software with other statistical software and analytical models. According to this view, mapping, query, and spatial modelling functions of GIS can provide data display at different scales, preprocessing, and data input for environmental and statistical models. The general objective of Multi-criteria Decision Making (MCDM) is to assist the decision-maker (DM) in selecting the “best” alternative from the number of feasible choice-alternatives under the presence of multiple choice criteria and diverse criterion priorities. The problem of multicriterion choice in decision making is the paramount challenge faced by individiuals, public and private corporations. The nature of challenge is two-fold: How to identify choice alternatives satisfying the objectives of parties involved in the decision-making process? How to order the set of feasible choice alternatives to identify the most preferred alternative? The challenge of multicriterion choice can be attributed to many spatial decision-making problems involving search and location/allocation of resources. These problems, often analysed in (GIS), include location/site selection for: service facilities, retail outlets, critical areas for specific resource management, and emergency service locations where are key locations for effective emergency management. In this study, the criteria and its priorities/weights that should be considered for finding optimal locations of fire stations are determined; and multicriteria site analysis is conducted based on mentioned criteria weights in (GIS) environment. Moreover, in order to test the sensitivity and robustness of the model developed, a sensitivity analysis is performed based on the combination of the criterion weights by using (GIS) capabilities. With these analyses performed, it is focused on the creating themodel that supports decision makers in decisionmaking for finding theoptimal locations of fire stations. In this study, these steps are followed: Definition of the problem/objective (determining the optimal locations of fire stations); determining the potential criteria in finding the optimal locations of fire stations; data collection and preparation and transfer to (GIS) environment; creation of raster data sets representing the regionalised criteria; classification of raster data sets; establishment of preference matrix, assigning preference values to the relevant criteria by using the pairwise comparison feature of Analytic Hiyerarchy Process (AHP); determination of criteria weights by calculating eigenvalues and eigenvectors of the preference matrix which evaluated by two decision maker group; determining the criteria priorities/weights values by using the synthesis of priorities and calculating the overall composite weights; calculating the result raster (suitability map for potential fire stations) as a weighted summation of all criteria raster data sets; conducting the sensitivity analyses in (GIS) environment in order to test the sensitiveness and robustness of the model developed; offering a system that supports decision makers in determining the optimal locations of fire stations. The integration of the (AHP) and (GIS) combines decision support methodology with powerful visualisation and analysing capabilities which should considerably facilitate finding optimal locations of fire stations and this process improves the decision making in emergency management. [ABSTRACT FROM AUTHOR]