Burak Kotan

Solar Cycle 25 has exceeded initial intensity predictions, characterized by a series of severe geomagnetic disturbances that have expanded the auroral oval well into mid-latitudes. This study presents a comparative analysis of the magnetospheric and ionospheric responses to three distinct events observed over the European sector, including regions such as Türkiye: the geomagnetic storms of November 2023, May 2024, and October 2024. By integrating Global Navigation Satellite System (GNSS)–derived Total Electron Content (TEC) data with ground-based magnetometer observations, the spatiotemporal evolution of these disturbances is characterized. All three events were driven by Coronal Mass Ejections (CMEs), yet their impacts on the mid-latitude ionosphere differed substantially. The May 2024 extreme storm was marked by prolonged negative ionospheric phases and severe TEC depletions over Europe, including Türkiye, primarily linked to compositional changes and disturbance dynamo electric fields (DDEFs). In contrast, the November 2023 storm exhibited coherent magnetic field depressions and rapid recovery phases, indicating a dominant role of prompt penetration electric fields (PPEFs). This comparative framework is extended to the October 2024 storm. Moreover, analysis of the vertical (Z) and horizontal (H) geomagnetic field components recorded at European observatories provides insight into the penetration of high-latitude current systems into lower latitudes during intense space weather events. The results demonstrate that mid-latitude regions experience complex electrodynamic coupling during the solar cycle maximum, governed by multiple mechanisms ranging from prompt electric field penetration to thermospheric heating across the Eastern Mediterranean sector.

Solar superflares of S‐class (>X10 in soft X‐rays) pose extreme space weather hazards, yet their prediction remains a fundamental challenge owing to their rapid and transient natures and the limitations of conventional event‐based forecasts. We introduce for the first time, a probabilistic spatiotemporal framework designed to identify extended epochs and heliographic zones with heightened superflare likelihood, rather than predicting individual events. Our methodology analyzes nearly five decades of Geostationary Operational Environmental Satellites SXR observations (1975–2025) and introduces two novel components: (a) a cross‐Laplacian diagnostic, derived from the butterfly diagrams of sunspot area and SXR flare density, which identifies photosphere‐corona coupling and pinpoints magnetic energy accumulation sites as preferential zones for S‐flare occurrence; and (b) a temporal model based on the coupled phase states of 1.7‐year and 7‐year oscillations, extracted via wavelet analysis and projected forward using a Bayesian Least‐Squares Support Vector Machine method. Integrating these spatial and temporal diagnostics yields a unified forecast, we identify 2025.7–2026.6 and 2027.2–2027.9 years, as the primary high‐probability windows for S‐class flare activity during Solar Cycle 25, corresponding preferentially to latitudinal bands in the southern (S–S) and northern (N–N) hemispheres, respectively. Subsequent windows are projected for Cycle 26. This physics‐informed, probabilistic approach provides a robust strategy for anticipating periods of extreme solar activity by characterizing the inherent patterns of magnetic energy storage and release in the solar atmosphere.

Nüfus artışıyla birlikte kentleşme, sanayileşme ve taşıt sayısındaki artışlar hava kirliliğinin artmasına sebep olmaktadır. Hava kirliliği insan ve çevre sağlığına zarar vermektedir. Bu nedenle bu çalışmada hava kirliliğinin önlenmesi, tedbirlerin alınması ve planlamaların yapılabilmesi için 1 yıl önceden hava kirliliğinin tahmin edilmesi amaçlanmıştır. Hava kirliliği parametrelerinden olan PM10 ve SO2 parametrelerinin mevsimsel ortalamalarının tahmin edilmesi için çoklu doğrusal regresyon analizi ve yapay sinir ağı yöntemleri kullanılmıştır. Kocaeli, Türkiye’nin sayılı sanayi bölgelerinden olması dolayısı ile çalışma alanı olarak seçilmiştir. Tahmin modellerinde meteorolojik veriler, kirletici konsantrasyonları, kentleşme, sanayileşme, topografik ve demografik veriler kullanılmıştır. Çalışmada enterpolasyon yöntemleri ile verilerin sürekliliğinin sağlanmasında, veri setlerinin oluşturulmasında ve haritaların yapılmasında coğrafi bilgi sistemleri kullanılmıştır. Çalışmada 2008 ile 2017 yılları arasındaki veriler ile tahmin modelleri oluşturulmuştur. Oluşturulan modellere 2018 verileri sunularak 2019 yılına ait tahmin değerleri elde edilmiştir. Elde edilen tahmin değerleri 2019 yılına ait gerçek değerler ile karşılaştırılmıştır. Model performansları ortalama mutlak yüzde hata (OMYH) değerine göre değerlendirilmiştir. Çalışma sonucunda yapay sinir ağının çoklu regresyon analizine göre performansının daha yüksek olduğu tespit edilmiştir. Ayrıca, mekânsal verilerin hava kirliliği tahmin modellerinde coğrafi bilgi sistemleri ile kullanılabilirliği gösterilmiştir.

The Covid-19 epidemic has adversely affected the world in terms of health, education, economic, tourism, social and psychological. During to the epidemic, different measures were taken to prevent the epidemic, such as travel bans, curfews, stopping in production. These measures have reduced and improved air pollution. Within the scope of this study, the change in air pollution in Kocaeli between 2019 and 2021 was examined monthly. PM10 and SO2 maps were created with inverse distance weighted (IDW) technique using geographic information systems technology (GIS). The year 2020, when Covid-19 measures were taken, was compared with 2019 and 2021. Change maps were created by taking the difference between 2020-2019 and 2021-2020 with GIS technology. As a result of the research, it was determined that the level of air pollution decreased in 2020. On the contrary, in 2021, an increase in air pollution levels was observed. In the study, a decrease was observed in PM10 concentration during the Covid-19 lockdowns, however a decrease was not observed for SO2.

Since December 2017, with the support of Kocaeli Metropolitan Municipality and with the leadership of Kocaeli University Geomatic Department, TÜBİTAK project numbered 117Y155 and titled “Monitoring of Vertical Displacements in East Marmara (Izmit Bay) with Multi-Band InSAR and GNSS Techniques, Investigation of the Relationship Between Ground Collapse and Building Density and Liquefaction" studies have been initiated to determine the risks in the Izmit Bay area. In this context, the spatial distribution of the building density analyzes and the spatial distribution of liquefaction around the gulf region were handled and integrated in the GIS environment within the scope of the project. The spatial distributions of the building floors were examined by spatial autocorrelation studies, taking into account the 3rd dimension of the buildings for the extraction of information about the load on the ground. Among the spatial autocorrelation analysis, Local Moran’s I statistics were taken into consideration comparatively. In addition, the liquefaction potential observed around the bay and its surroundings is evaluated for 0-3 m, 3-6 m, 6-9 m, 9-12 m from 33-35 m depths. Liquefaction areas and risky areas are defined by categorizing liquefaction values for different depths according to liquefaction potential thresholds. As a result, buildings in liquefaction risk areas were determined. The study is important in terms of taking sustainable precautions against future natural disasters, preparing precaution plans for the speed and density of construction, determining the current situation of building and ground liquefaction around Gölcük and Izmit Bay, taking engineering measures in risky areas and providing building and life safety and creating a basis for disaster assessments.