Every man-made structure creates certain risks -dams are no exception. Most failures in man-made structures that have occurred could have been avoided if the structures' behaviour had been inspected, monitored, and analyzed continuously, and if proper corrective measures had been taken in a timely fashion. The DSI (The General Directorate of State Hydraulic Works), which is the institution responsible for dam safety, has long used surveying methods to measure the displacements of geodetic points as a part of dam monitoring policy. In this study, we focus on the dam's mechanical behaviour throughout a time period of more than 10 years. These study results have been derived from a separate, ongoing project that has monitored deformation on the Ataturk Dam and is now determining the water level of the reservoir. The project results show that although the dam body has become more stable and the water load behind the dam has increased, the rate of displacement of the dam has declined significantly. From these results, it can be seen that the reservoir water level can be increased evenly over time and that 542 m is the maximum water level of the dam's reservoir.
Social media (SM) can be an invaluable resource in terms of understanding and managing the effects of catastrophic disasters. In order to use SM platforms for public participatory (PP) mapping of emergency management activities, a bias investigation should be undertaken with regard to the data related to the study area (urban, regional or national, etc.) to determine the spatial data dynamics. Thus, such determinations can be made on how SM can be used and interpreted in terms of PP. In this study, the city of Istanbul was chosen for social media data research area, as it is one of the most crowded cities in the world and expecting a major earthquake. The methodology for the data investigation is: 1. Obtain data and engage sampling, 2. Identify the representation and temporal biases in the data and normalize it in response to representation bias, 3. Identify general anomalies and spatial anomalies, 4. Manipulate the trend of the dataset with the discretization of anomalies and 5. Examine the spatiotemporal bias. Using this bias investigation methodology, citizen footprint dynamics in the city were determined and reference maps (most likely regional anomaly maps, representation maps, time-space bias maps, etc.) were produced. The outcomes of the study can be summarized in four steps. First, highly active users generate the majority of the data and removing this data as a general approach within a pseudo-cleaning process means concealing a large amount of data. Second, data normalization in terms of activity levels, changes the anomaly outcome resulting from diverse representation levels of users. Third, spatiotemporally normalized data present strong spatial anomaly tendency in some parts of the central area. Fourth, trend data is dense in the central area and the spatiotemporal bias assessments show the data density varies in terms of the time of day, day of week and season of the year. The methodology proposed in this study can be used to extract the unbiased daily routines of the social media data of the regions for the normal days and this can be referred for the emergency or unexpected event cases to detect the change or impacts.applications and is referred to as Volunteered Geographic Information (VGI) [4,5]. The users can be thought of as unconscious volunteers for social media (SM) VGI, as deliberate volunteers for peer production VGI and as public participators for in citizen science based VGI [6][7][8]. The way of producing these forms of VGI is referred to as neo-geography in that it adopts neo-geographers (i.e., volunteers) who contributes to mapping activity without being expert [9]. This inexperience with regards to data production is questioned in the context of data quality [10][11][12], demographic bias (such as, gender, socioeconomic and educational aspects) [13,14] sampling bias (referring to volunteer sampling) and its impact on the generated data [15,16].In their very first form, citizen science projects in the very first forms were carried out with the use of paper maps [1]. However, with the...
Türkiye tarih boyunca pek çok farklı medeniyete ev sahipliği yapmış bir ülke olması dolayısıyla bu medeniyetlere ait, oldukça çeşitli kültürel miras ögelerini içinde barındırmaktadır. Bu bağlamda, sahip olduğumuz bu kültürel mirasın korunması ve gelecek nesillere ulaştırılması oldukça önem arz etmektedir. Kültürel miras ögeleri zaman içerisinde birçok farklı sebepten ötürü deformasyona uğrayabilmekte veya tamamen yok olabilmektedir. Bu durumun önüne geçilebilmesi için öncelikle bu kültürel miras ögelerinin 3 Boyutlu (3B) dokümantasyonunun (belgelenmesinin) yapılması gerekmektedir. Dokümantasyon sonucunda elde edilen veriler, kültürel miras ögeleri üzerindeki deformasyonların izlenmesi, restorasyon çalışmaları gibi pek çok farklı alanda katkı sunabilmektedir. Günümüzde dokümantasyon çalışmalarında kullanılan farklı yöntemler mevcuttur. Arazi üzerinde veri toplaması aşamasında kullanılan yöntemlerden birkaçı; yersel fotogrametri, hava fotogrametrisi ve yersel lazer tarama (YLT) olarak gösterilebilir. Öte yandan, arazi çalışması aşamasında toplanılan verilerin işlenebileceği birçok yazılım mevcuttur. Bu çalışmada, İstanbul Fatih ilçesi sınırlarındaki Sultanahmet Meydanı’nda bulunan tarihi Dikilitaş’ın YLT tekniği kullanılarak taraması yapılmış ve üç farklı yazılım aracılığıyla nokta bulutu üretilmiştir. Bu yazılımların kültürel mirasın dokümantasyonu çalışmalarında kullanılabilirlikleri karşılaştırmalı olarak irdelenmiştir.
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