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Master Thesis Uzaktan Algılama ve Cbs Tekniğini Kullanarak Irak'ın Al-muhammadi Vadisinde Yağmur Suyu Toplama Yapısının Yerinin Belirlenmesi(2020) Khudhaır, Mohammed Abbas; Darama, Yakup; Sayl, Khamis NabaYağış, yarı kurak ve kurak bölgelerde su kıtlığı prsorununu azaltmada kilit bir kaynaktır. Yağmur suyunun toplanması, yağmur suyunun korunması için önemli bir araçtır. Yağmur suyu toplama yapısı için uygun bir yer, suyun mevcudiyetinin arttırılmasında ve su kaynakları planlamasının iyileştirilmesinde önemli bir rol oynamaktadır. Bu çalışmanın amacı, ArcGIS'te ModelBuilder ile oluşturulan uygunluk modelini kullanarak yağmur suyu toplama yapısı için doğru yerinin belirlenmesidir. Yağmur suyu toplama yapısının en uygun yerini belirlemek için çalışmada toprak yapısı, eğim, drenaj yoğunluğu, bitki örtüsü, yollara uzaklık ve akış derinliği olmak üzere altı tematik katman kullanılmıştır. Bu yöntem Irak'ın Al-Anbar eyaletinin El Muhammadi Vadisi'ne uygulanmıştır. Sonuçların analizinden, yağmur suyu toplama yapısının yeri için çalışma alanının %12 si uygun alan, %42 orta uygun alan ve %46 lık bölümününde uygun olamayan alanı temsil ettiğini belirlenmiştir. Bu çalışma, Coğrafi Bilgi Sistemlerinin geniş alanları taramak için esnek, uygun maliyetli ve zaman tasarrufu sağlaması açısında uygun bir araç olduğunu göstermiştir. Bu yöntemin uygulanması yamur suyu hasadı için yer seçiminde Bu planın uygulanması, yağmur suyu hasadının yer seçimi politika kabulüne dahil edilmelidir. Anahtar Kelimeler: Uzaktan Algılama, CBS, Yağmursuyu hasadı, Al-Muhammadi VadisiArticle Citation - WoS: 13Citation - Scopus: 16Determination of Sediment Deposition of Hasanlar Dam Using Bathymetric and Remote Sensing Studies(Springer, 2019) Darama, Yakup; Selek, Zeliha; Selek, Bulent; Akgul, Mehmet Ali; Dagdeviren, MuratHasanlar Dam and Hydroelectric Power Plant are located on Kucuk Melen Creek in the Western Black Sea Basin of Turkey. The dam was constructed in 1974 to provide domestic water needs of the Duzce Province, to supply irrigation water need, to control and mitigate floods and to produce hydroelectric power. This dam has been subjected to severe sedimentation since its construction in 1974. Therefore, bathymetric field survey studies were conducted to determine storage loss in the Hasanlar Dam reservoir by sedimentation. Bathymetric survey data from the reservoir site of the Hasanlar Dam were obtained in 1979, 1999 and 2014. Analysis of the bathymetric data, GIS and remotes sensing techniques showed that storage loss in reservoir active volume between 1974 and 1999 was 24% and between 1974 and 2014 storage loss was 26%. Analysis of the bathymetric maps also showed that sediment accumulation is severe near and around the dam body and the spillway whose discharge capacity was decreased by sediment accumulation. This is extremely critical because the flood of May 1998 caused the high risk of collapse of dam due to reduced capacity of the spillway. Remote sensing technique was used to determine the future deposition of sediment in the reservoir. For this purpose, 35 points in the reservoir area were determined by comparing the relative water depths and actual water depths using satellite image of the bathymetry in July 2017 and Lake Observation Station. High correlation (R-2=0.833) was calculated by using logarithmic nonlinear regression analysis between actual and relative water depths for those 35 control points. The average of absolute values of differences between the estimated and actual water depths was found as 1.06m, and RMSE was calculated as 1.25m. This analysis shows that in the future, remote sensing data can be used in the studies of determining the depth of water and the total sediment thickness. In addition, the volume of the entire reservoir can be predicted by measuring the actual water depth only at those 35 control points without making a bathymetric map of the whole dam reservoir.Article Citation - WoS: 10Citation - Scopus: 12Anomaly Detection With Low Magnetic Flux: a Fluxgate Sensor Network Application(Elsevier Sci Ltd, 2016) Ege, Yavuz; Coramik, Mustafa; Kabadayi, Murat; Citak, Hakan; Kalender, Osman; Yuruklu, Emrah; Nazlibilek, SedatRecent studies on remote detection methods were mostly for improving variables like sensing distance, sensitivity and power consumption. Especially using anisotropic magneto-resistive sensors with low power consumption and high sensitivity for detecting subsurface magnetic materials became very popular in last decades. In our study, for detecting subsurface materials, we have used fluxgate sensor network for having even higher sensitivity and also minimizing the power consumption by detecting the changing rates of horizontal component of earth's magnetic flux which is assumed to be very low. We have constituted a magnetic measurement system which comprises a detector system, which has a mechanism enables sensors to move in 3-D space, a data acquisition module for processing and sending all sensor information, and a computer for running the magnetic flux data evaluation and recording software. Using this system, tests are carried out to detect anomalies on horizontal component of earth's magnetic flux which is created by different subsurface materials with known magnetic, chemical and geometric properties. The harmonics of horizontal component of earth's magnetic flux in scanned area are analyzed by the help of DSP Lock-In amplifier and the amplitudes of high variation harmonics are shown as computer graphics. Using the graphic information, the upside surface geometry of subsurface material is defined. For identifying the magnetic anomalies, we have used the scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor. We used an algorithm for matching the newly scanned image to the closest image in database which is constituted of mines and possible other metal objects like cans, etc. Results show that, if the proposed detection system is used instead of metal detectors which cannot distinguish mines from other metal materials and alert for every type of metal with different geometries, it can be said that miss alarm count, work force and time can be decreased dramatically. In this paper, mostly the setup of the system is described and in Appendix A some experimental outputs of the system for different geometries of metal samples are given. And also for comparing the results of the proposed system, additional experiments are carried out with a different type of sensor chip, namely KMZ51, and also given in Appendix A. (C) 2015 Elsevier Ltd. All rights reserved.Article Citation - WoS: 19Citation - Scopus: 19Identification of Materials With Magnetic Characteristics by Neural Networks(Elsevier Sci Ltd, 2012) Nazlibilek, Sedat; Ege, Yavuz; Kalender, Osman; Sensoy, Mehmet Gokhan; Karacor, Deniz; Sazh, Murat HusnuIn industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.
