Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 19Citation - Scopus: 18An Appraisal of the Local-Scale Spatio-Temporal Variations of Drought Based on the Integrated Grace/Grace-fo Observations and Fine-Resolution Fldas Model(Wiley, 2023) Khorrami, Behnam; Ali, Shoaib; Gündüz, OrhanThe gravity recovery and climate experiment (GRACE) observations have so far been utilized to detect and trace the variations of hydrological extremes worldwide. However, applying the coarse resolution GRACE estimates for local-scale analysis remains a big challenge. In this study, a new version of the fine resolution (1 km) Famine early warning systems network Land Data Assimilation System (FLDAS) model data was integrated into a machine learning model along with the GRACE data to evaluate the subbasin-scale variations of water storage, and drought. With a correlation of 0.99 and a root mean square error (RMSE) of 3.93mm of its results, the downscaling model turned out to be very successful in modelling the finer resolution variations of TWSA. The water storage deficit (WSD) and Water Storage Deficit Index (WSDI) were used to determine the episodes and severity of drought events. Accordingly, two severe droughts (January 2008 to March 2009 and September 2019 to December 2020) were discerned in the Kizilirmak Basin (KB) located in Central Turkiye. The characterization of droughts was evaluated based on WSDI, scPDSI, and model-based drought indices of the soil moisture storage percentile (SMSP) and groundwater storage percentile (GWSP). The results indicated discrepancies in the drought classes based on different indices. However, the WSDI turned out to be more correlated with GWSP, suggesting its high ability to monitor groundwater droughts as well.Article Citation - WoS: 5Citation - Scopus: 7Fast Texture Classification of Denoised Sar Image Patches Using Glcm on Spark(Türkiye Klinikleri Journal of Medical Sciences, 2020) Özcan, Caner; Ersoy, Okan; Oğul, İskender ÜlgenClassification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library. The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results, and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method. TerraSAR-X images of high-resolution real-world SAR images were used as data.Article Citation - WoS: 5Citation - Scopus: 5Chemometric Analysis of Chemo-Optical Data for the Assessment of Olive Oil Blended With Hazelnut Oil(Stazione Sperimentale per le Industrie, 2019) Kadiroğlu, Pınar; Korel, Figen; Pardo, MatteoThe main objective of this study was to determine different hazelnut oil concentrations in extra virgin olive oil (EV00) belonging to different geographical regions inside Turkey using the combination of a SAW sensor based electronic nose (e-nose) and a machine vision system (MVS). We leveraged the oil characterisation given by the two easy-to-use and complementary experimental techniques through the adoption of conventional PCA for data exploration and random forests (RF) for supervised learning. The e-nose/MVS combination allows significantly better results both in adulteration detection independently of EVOO's geographical provenance and in EVO0 geographical provenance determination, independently of the adulteration level, with respect to the single characterisation method. RF analysis also produces feature ranking, permitting to shed light on which oils' characteristics influence the learning result. We found that EV00 geographical provenance discrimination is mainly due to yellowness and guaiacol content, while (E)-2-hexenal chiefly determines the prediction of the hazelnut level.