Astronomical Site Selection for Turkey Using GIS Techniques

A site selection of potential observatory locations in Turkey have been carried out by using Multi-Criteria Decision Analysis (MCDA) coupled with Geographical Information Systems (GIS) and satellite imagery which in turn reduced cost and time and increased the accuracy of the final outcome. The layers of cloud cover, digital elevation model, artificial lights, precipitable water vapor, aerosol optical thickness and wind speed were studied in the GIS system. In conclusion of MCDA, the most suitable regions were found to be located in a strip crossing from southwest to northeast including also a diverted region in southeast of Turkey. These regions are thus our prime candidate locations for future on-site testing. In addition to this major outcome, this study has also been applied to locations of major observatories sites. Since no goal is set for \textit{the best}, the results of this study is limited with a list of positions. Therefore, the list has to be further confirmed with on-site tests. A national funding has been awarded to produce a prototype of an on-site test unit (to measure both astronomical and meteorological parameters) which might be used in this list of locations.Comment: 17 pages, 10 figures, accepted by Experimental Astronom

Estimation of sunshine duration with artificial neural networks by using NWCSAF cloud type product.

TEZ10401Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2015.Kaynakça (s. 61-63) var.xiii, 73 s. : res. (bzs. rnk.), tablo ; 29 cm.Güneşlenme süresi iklim, enerji, tarım ve sağlık gibi farklı alanların çalışmalarında yer alan çok önemli bir veridir. Bu yüzden zamansal ve alansal dağılımının bilinmesi ve belirlenmesi kritik önem taşır. Ülkemizde güneşlenme süresi ölçümleri meteorolojik istasyonlarda helyograflarla yapılmaktadır. Bu ölçümler noktasal olduğundan dolayı ölçüm yapılan noktalar arasında kalan alanlar için tahmin edilmesi gerekir. Güneşlenme süresini etkileyen en önemli faktörler, ilgilenilen alanın coğrafi konumu, üzerindeki bulutluluk oranı ve bulutun tipidir. Coğrafi konumu ilgilendiren hesaplamalar astronomik denklemler yardımıyla büyük bir doğrulukla hesaplanabilirken bulutluluk oranının ve bulut tipinin belirlenmesi ve etkilerinin anlaşılması daha zordur. Çünkü bulutluluk zamansal ve alansal olarak değişkenlik gösterir ve ayrıca farklı bulut tipleri de güneşlenme süresini farklı oranlarda etkiler. Bu çalışmada, Türkiye'nin tamamına ait aylık ortalama güneşlenme süresi; Meteosat uydularına ait verilerden üretilen NWCSAF ""Bulut Tipi"" ürünü (PGE02) ve yer gözlemi kayıtları kullanılarak yapay sinir ağı yöntemiyle tahmin edilmiştir.Sunshine duration is a very important parameter in many different fields of applications such as climate, renewable energy, agriculture and health. In this respect the estimation and determination of the temporal and spatial variability of this parameter is critical. Sunshine duration have been measured by heliographs in Turkey. Since these measurements are point-wise, estimations need to be done for other locations where there is no measurement available. The geographical location, total cloudiness and type of the clouds are the most dominant factors affecting the sunshine duration for a given place. The geographical affects are possible to calculate by using the astronomical equations while the cloudiness and cloud type factors are harder to estimate and understand. This is mostly because of the temporal and spatial variations of the cloud parameters and their different effects on the sunshine duration. In this study, sunshine duration for all over the Turkey is estimated with artificial neural networks by using the meteorological station measurements and NWCSAF Cloud Type (PGE 02) product which produced by Meteosat satellite data.Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi tarafından desteklenmiştir. Proje No: FYL2014-3286

Housekeeping with multiple autonomous robots: representation, reasoning and execution

We formalize actions and change in a housekeeping domain with multiple cleaning robots, and commonsense knowledge about this domain, in the action language C+. Geometric reasoning is lifted to high-level representation by embedding motion planning in the domain description using external predicates. With such a formalization of the domain, a plan can be computed using the causal reasoner CCALC for each robot to tidy some part of the house. We introduce a planning and monitoring algorithm for safe execution of these plans, so that it can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. We illustrate the applicability of this algorithm with a simulation of a housekeeping domain

Çözüm kümesi programlama kullanarak ortaklaşa ev içi hizmet robotiği (Collaborative housekeeping robotics using answer set programming)

Answer Set Programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers; it is applied to solve hard problems in various domains, such as, systems biology, wire routing, space shuttle control. In this paper, we present an application of ASP to housekeeping robotics, by showing how the following problems are addressed using computational methods/tools of ASP: 1) embedding commonsense knowledge automatically extracted from the commonsense knowledge base ConceptNet, into high-level representation, 2) embedding (continuous) geometric reasoning and temporal reasoning about durations of actions, into (discrete) high-level reasoning. We illustrate the applicability of ASP on several housekeeping robotics problems

Housekeeping with multiple autonomous robots: representation, reasoning and execution

We formalize actions and change in a housekeeping domain with multiple cleaning robots, and commonsense knowledge about this domain, in the action language C+. Geometric reasoning is lifted to high-level representation by embedding motion planning in the domain description using external predicates. With such a formalization of the domain, a plan can be computed using the causal reasoner CCALC for each robot to tidy some part of the house. We introduce a planning and monitoring algorithm for safe execution of these plans, so that it can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. We illustrate the applicability of this algorithm with a simulation of a housekeeping domain

Evaluating the utility obtained by using radar based precipitation for prediction of flood events in western Black Sea basins of Turkey

Climate change has a direct influence on the hydrological cycle and its elements. Consequently, extreme events are expected to occur more frequently at different times and locations on the Earth and become more catastrophic.Hence, it is critically important to develop systems to accurately forecast rising water levels of streams and rivers prior to occurrence of dangerous conditions. Real-time flood forecasting systems are becoming a critical tool for emergency preparedness and decision making. Given radar observations may provide high spatial and temporal resolution precipitation information, these observations are commonly used to estimate precipitation for short-term period (1-2 h) and to monitor the track and extent of the precipitation events. Therefore, their use in flood prediction becomes very critical. With the aim of developing a fully coupled atmosphere-hydrology model system, the Weather Research and Forecasting (WRF) model is enhanced by integrating a new set of hydrologic physics parameterizations (WRF-Hydro) accounting for lateral water flow occurring at the land surface. In addition to precipitation input derived from WRF, in simulating flood events with one-way coupled system, radar-derived precipitation will also be used as input to the modeling system. The performance of the modeling systems will be evaluated for the selected heavy rainfall events and associated flooding conditions over Western Black Sea basins in Turkey. Radar based precipitation information is expected to improve the accuracy of the flood simulations