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Bu çalışma, 24. güneş döngüsünde meydana gelen 01 Şubat 2017 (DsT -45 nT) zayıf jeomanyetik fırtınasının matematiksel analizini amaçlamaktadır. NASA'dan alınan güneş rüzgarı parametreleri (Bz, E, P, N, v, T), zonal jeomanyetik indeksleri (Dst, ap, AE) ve neden-sonuç ilişkisini göz önünde bulundurarak Şubat fırtınasının matematiksel modeli ortaya çıkarılmıştır. Makalede fırtına titizlikle analiz edilir ve nedensellik ilkesinin yönettiği modellerle özellikleri ortaya çıkarılmaya çalışılır. Bu çalışmada değişkenlerin değer aralığı ve standart sapmaları tanımlayıcı analiz ile tanımlanır, verilerin binary ilişkileri kovaryans matrisi ile gösterilir ve yine verilerin hiyerarşik kümeleri dendrogram ile tanıtılır. Faktör analizi, verilerin normal dağılımları yardımıyla gerçekleştirilir ve fenomenin gizemi lineer-nonlineer modellerle tartışılmaya çalışılır. Değişkenlerin ikili görünümleri grafikler ile okuyucuya sunularak fırtınaya görsellik kazandırılır. Bu çalışma, literatürde sınırlı sayıda çalışma bulunan zayıf seviyede manyetik fırtınanın modellenmesi konusuna katkı sağlamaktadır.

Finding an accurate computational method for estimating pan evaporation (EPm) can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called multiple model-support vector machine (MM-SVM) with the aim of showing the increasing, decreasing, and constant accuracy behavior of this hybrid model and improving the results of estimating EP compared to the two models ANN and SVM on a monthly scale of EPm in four meteorological stations (Ardabil, Khalkhal, Manjil (from Iran), and Grand Island (from the USA)) located in semi-arid regions, using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)), was evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE), and coefficient of determination (R2)) and with the help of case visual indicators were compared. According to the results of evaluation indicators in the test phase, MM-SVM-6, ANN-5, MM-SVM-3, and MM-SVM-7 with RMSE = 1.088, 0.761, 0.829, and 0.134 mm/day; MAE = 0.79, 0.54, 0.589, and 0.105 mm/day; KGE = 0.819, 0.903, 0.972, and 0.981; and R2 = 0.939, 0.962, 0.967, and 0.996 and with four input variables were introduced as the best models in Ardabil, Khalkhal, Manjil, and Grand Island stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs estimated by independent models, its power to estimate EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant, and decreasing modes in EPm estimation accuracy by this hybrid model under the semi-arid climatic conditions of the studied areas were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.

This paper discusses the diurnal and seasonal variations of the F2 layer critical frequency (foF2) and the improvement of performance of the IRI-2016 model in predicting foF2 over three latitudes in different hemispheres during low and high solar activities. We extracted the foF2 data from six ionosonde stations which are Manila (14.7 degrees N, 121.1 degrees E), Yamagawa (31.2 degrees N, 130.6 degrees E), Yakutsk (62.0 degrees N,129.6 degrees E), Townsville (19.6 degrees S, 146.8 degrees E), Hobart (42.9 degrees S, 147.3 degrees E) and Terre Adelie (66.6 degrees S, 140.0 degrees E). The data of both low solar activity (LSA) period and high solar activity (HSA) periods were divided into three seasons as Northern Summer (May, June, July and August), Equinoxes (March, April, September and October) and Northern Winter (November, December, January and February). The present study showed that the IRI-2016 performance is strongly dependent on the solar activity, latitude, season, local time and hemisphere. For both hemispheres, the foF2 values at low latitude station are larger than those at middle latitude station, whereas the foF2 values at middle latitude station are larger than those at high latitude station. The agreement between IRI2016-modelled foF2 and foF2 measurements on all stations selected in the northern hemisphere is best for North Summer and worst for North Winter. For northern hemisphere, the values of relative deviations during both solar activities are largest in high latitudes and smallest in middle latitudes. As for southern hemisphere, the values of relative deviations during LSA are largest in middle latitudes and smallest in high latitudes, whereas the values of relative deviations during HSA are largest in low latitudes and smallest in high latitudes. It is thought that the relative deviations in the observed foF2 values are caused by solar activity that strongly alter chemical and electromagnetic processes in the ionosphere. These results are important for future improvements depending on solar activity and seasons in the IRI model for foF2 values over three latitudes in different hemispheres.

The ionosphere and its activities play an important role in radio communications, space science and positioning. In this paper, we present efficiency of the adaptive neuro-fuzzy inference system (ANFIS) in ionosphere time series prediction at severe solar activity periods. The high accuracy prediction of the ionosphere total electron content (TEC) using ANFIS is the main purpose of the paper. Also, investigation of the daily, monthly and annually temporal behavior of the ionosphere is another goal of the paper. Three GPS stations (ANKR, ANTL and ZONG) from Turkish permanent GPS network (TPGN) are selected for analysis. These stations are at mid-latitudes. To predict and analyze ionospheric temporal behavior, observations of the 2014–2015 are selected. The index of solar activity in these 2 years is the maximum. Training of ANFIS is done by back-propagation (BP) algorithm. In order to analyze the accuracy of the proposed method, testing step has been performed in different days, months and seasons and compared with the global ionosphere map (GIM) TEC as a traditional ionosphere model, as well as, international reference ionosphere 2016 (IRI2016) and artificial neural network (ANN) models. To validate the results, root mean square error (RMSE), correlation coefficient, relative error, dTEC = |TECGPS-TECMODEL| and residual histogram are used. At testing step, the maximum correlation coefficient obtained 0.869 for the ANFIS. Also, the minimum RMSE of 3.131 TECU is computed for ANFIS at testing step. Using the ANFIS model, the daily, monthly and annual variations of the ionosphere time series are revealed with high accuracy. The results show that the ANFIS model is a reliable and high-precision alternative to the conventional ionosphere models in Turkish region.

In this paper, using the Total Electron Content (TEC) data obtained from GUAM (13.58 degrees N, 144.86 degrees E) and PNGM (2.04 degrees S, 147.36 degrees E) Global Positioning System (GPS) stations, the seasonal characteristics of equatorial plasma bubbles (EPBs) in conjugate hemispheres during the Solstices and Equinoxes of 2015 are examined. The EPBs were determined using the rate of TEC (ROT) and rate of TEC index (ROTI) indices, which are good indicators for the ionospheric instabilities and behavior of EPBs. The results from all analysis were compared with each other according to hours, seasons and hemispheres. The EPBs for all seasons were detected at the postsunset local times. The intensity level of ROTI and ROTIave shows variations during Equinoxes. For both hemispheres, the occurrence probability of EBPs during the Equinoxes is a maximum of 22% while the occurrence probability of EBPs during the Solstices is a maximum of 5%. The severity of EPBs was larger during March while severity of EPBs was smallest during December. It was also observed that the EPBs during Equinoxes were more predominant in the northern hemisphere while the EPBs during June were more predominant in the southern hemisphere.

The analysis of the unexpected ionospheric phases before large earthquakes is one of the cutting-edge issues in earthquake prediction studies. In this study, the total electron content (TEC) data from seven International GNSS Service (IGS) stations and the global ionosphere maps (GIMs) were used. Short-time Fourier transform (STFT) and a running median process were applied to the TEC time series to detect abnormalities before the Mw 7.3 Iran–Iraq border earthquake on 12 November 2017. The analyses showed positive anomalies 8–9 d before the earthquake and some positive and negative anomalies 1–6 d before the earthquake. These anomalies were cross-checked using the Kp, Dst, F10.7, Bz component of the interplanetary magnetic field (IMF Bz), electric field (Ey), and plasma speed (VSW) space weather indices. The results showed that the anomalies 1–6 d before the earthquake were caused by a moderate magnetic storm. Moreover, the positive anomalies 8–9 d before the earthquake were likely related to the Iran–Iraq border earthquake due to quiet space weather, local dispersion, and the proximity to the epicenter.

Son yıllarda internet üzerinden online GNSS değerlendirme servisleri konum elde etmede önemli bir araç olarak kullanılmaktadır. Bu sistemlerin en önemli özelliği GNSS verilerinin değerlendirme konusunda hiçbir bilgiye sahip olmayan kişilerin veriyi rahatlıkla değerlendirebilmesine olanak sunması ve ücretsiz olmasından dolayı ticari veya bilimsel yazılımın alınmasına gerek duyulmamasıdır. Bu çalışmada ANRK, ANTL, IZMI, SINP ve VAN1 istasyonlarına ait RINEX gözlemleri 1, 4, 8, 12, 16, 20 ve 24 saatlik dilimlere ayrılarak CSRS-PPP ve Trimble-RTX servislerine gönderilmiş ve elde edilen koordinatların doğruluk araştırılması yapılmıştır. Çalışma sonucunda CSRS-PPP servisinden elde edilen konum verisinin referans veriye daha yakın olduğu anlaşılmıştır.

İyonosfer tabakası yapısı gereği güneş aktivitesi, mevsimsel değişim, gece-gündüz, konum, jeomanyetik aktivite, deprem gibi etmenlere bağlı olarak değişim göstermektedir. Bu tabakada meydana gelen değişim çoğunlukla güneş kaynaklı olsa da sözü edilen diğer etkiler iyonosfer modellemesi için göz önünde bulundurulmalıdır. Günümüzün en büyük doğal afetlerinden biri olan deprem dünya genelinde çok sayıda can ve mal kaybına sebep olmaktadır. Depremle ilgili geçmişten bugüne çok sayıda bilim insanı çalışma yapmıştır. Yapılan çalışmalar sonucunda; deprem öncesi, anı veya sonrasında iyonosfer tabakasında bazı değişimler meydana geldiği, bu sebeple deprem tahmini çalışmalarında iyonosfer tabakasının incelenmesi gerekliliği ortaya koyulmuştur. Bu çalışma kapsamında deprem kaynaklı iyonosferik anomalilerin belirlenebilmesi için deprem öncesi, anı ve sonrasını kapsayacak şekilde toplam 60 günlük Toplam Elektron Miktarı TEC verisi kullanılmıştır. Alt sınır (LB) ve üst sınır (UB) değerlerinin hassas şekilde belirlenebilmesi için ilk 30 güne ilişkin TEC verilerinden faydalanarak deprem analizi için kullanılacak k değeri belirlenmiştir. Deprem analizi için kullanılacak k değeri elde edildikten sonra deprem kaynaklı TEC değişimlerinin belirlenebilmesi için LB ve UB değerleri her epok için üretilmiştir. Bu sayede iyonosfer tabakasına etki eden mevsimsel değişim ile küçük ve orta seviyedeki jeomanyetik etkilerin deprem kaynaklı iyonosferik değişim analiz sonuçlarına olan etkisinin azaltılması sağlanmıştır. 14 Kasım 2016 (yılın günü 319) tarihinde meydana gelen Yeni Zelanda depreminin merkez koordinatları -42,75 N, 173,07 E olan depremin büyüklüğü Mw 7,8 olarak belirlenmiştir. Yapılan analiz sonucunda 315 ve 317. günlerde pozitif anomaliler görülmüş ve anomali miktarları verilmiştir. Söz konusu anomalilerin depremle ilgili olup olmadığının anlaşılabilmesi için anomali görülen günlerde uzay iklim koşulları detaylı olarak irdelenmiştir. Ionosphere shows change depending on solar activity, seasonal variation, day-night, geomagnetic activity, earthquake. Even though the change in this layer is mostly caused by the sun, the other effects mentioned should be taken into consideration for ionosphere modeling. One of the natural disasters of modern-day world, the earthquake causes a large number of lives and property loss throughout the world. Many scientists have been involved in studies of earthquake from the past to the present. As a result of the studies; some changes in the ionosphere layer occurred before, during and after the earthquake, and thus ionosphere layer should be monitored effectively. In order to determine anomalies induced by the earthquake within the scope of this study, a total of 60 days of TEC data was used. In order to determine the boundaries of lower bound (LB) and upper bound (UB), the k value to be used for earthquake analysis was determined by using TEC data for the first 30 days. After obtaining the k value to be used for the earthquake analysis, LB and UB values have been produced for each epoch in order to determine the earthquake-induced ionosphere anomaly. In this way, the effect of seasonal change on the ionosphere layer and the effect of small and medium geomagnetic effects on earthquake-induced ionospheric change analysis results will be reduced. As a result of the analysis, positive anomalies were detected 4 days and 2 days before the earthquake respectively. In order to understand whether these anomalies are related to earthquakes or not, spaceweather conditions were examined in the days of anomalies detail.

Earthquake (EQ) anomalies in the form of enhancement and depletion in ionospheric Total Electron Content (TEC) from Global Positioning System (GPS) may considerably alarm about short and long term precursors of the impending main shock. In this paper, TEC anomalies are investigated from permanent GPS ground-stations in Turkey associated to Mw ≥ 6.0 EQs occurred in 2011–2012. Temporal and spatial analyses of TEC at 2 h sampling have shown significant evidences about EQ induced ionospheric anomalies during 10–14 h of UT (Universal Time) within 5 days before Mw 6.0 Greece, and Mw 7.1, Turkish EQ. Spatial analyses have manifested arrival of TEC anomalies at UT = 10 h to epicenter of both EQs, which linger above epicenter during UT = 12–14 h and left seismogenic zone after UT = 14 h before every EQ during Kp

08.09.2017 tarihinde gerçekleşen manyetik fırtınanın iyonosfer tabakasında sebep olduğu değişim, GNSS ölçüleri kullanılarak elde edilen.TEC değerleri üzerinden analiz edilmiştir. Kuzey ve güney kutbunda, iyonosferin farklı bölgelerinde bulunan GNSS istasyonlarına ait RINEX gözlemlerinden TEC değerleri elde edilmiştir. Manyetik fırtına kaynaklı tEC değişiminin ortaya çıkarılabilmesi için manyetik fırtına öncesi 10 gün, manyetik fırtına günü ve sonrası 1 gün olmak üzere her istasyona ilişkin toplam 12 günlük veri değerlendirilmiştir. Yapılan analiz sonucunda manyetik fırtınanın olduğu gün, öncesi ve sonrasında pozitif ve negative anomaliler elde edilmiştir.

This paper describes the investigation and comparison of two consecutive geomagnetic storms of Solar Cycle 24. The first storm is moderate (Dst = −50 nT) and occurred on 22 April 2017, while the second storm is intense (Dst = −125 nT) and occurred on 28 May 2017. These storms are compared on the basis of solar wind parameters (Bz, E, P, N, v, and T) and zonal geomagnetic indices (Dst, ap, and AE). Mathematical analysis is performed with a data set of 9 variables obtained from NASA's observations. Descriptive statistics, which demonstrates the power of binary relationships with a covariance matrix; hierarchical clustering with a dendrogram; and factor analysis pointing to three separate eigenvectors are the primary tools of our study analysis. From the results, linear models are established from the cause-effect relationship of zonal geomagnetic indices with solar wind parameters governed by the causality principle. Finally, nonlinear models are also established with solar wind pressure (P), proton density (N), and ap index. In all analyses, the causality principle is obeyed rigidly and the mathematical models are discussed in terms of the cause-effect relationship.

Water vapor (WV) is one of the most important parameters in meteorological studies. Using an adaptive neuro-fuzzy inference system (ANFIS), a new method has been proposed for spatiotemporal modeling of precipitable WV (PWV). In a first step, the tropospheric zenith wet delay (ZWD) is calculated using the observations of 23 GPS stations in the northwest of Iran. Out of these 23 stations, 21 stations for training and 2 stations for testing and validating were selected. The observations are for 15 days, ranging from day of year (DOY) 300 to 314 in 2011. The reason for choosing this area and time interval is the availability of a complete set of data. Then, the values of ZWD are converted to PWV. The PWV values obtained from this step are considered as the output of the ANFIS. Also, the latitude and longitude values of the GPS stations, the DOY, observational time (min), temperature (T), pressure (P), and relative humidity (RH) are considered input to ANFIS. The ANFIS network is trained using the back-propagation algorithm. After the training step, the PWV values are evaluated at 2 test stations, KLBR and GGSH, and at Tabriz radiosonde station (38.08° N, 46.28°E). For a more accurate evaluation, all the results of the new method are compared with the voxel-based tomography model. The evaluation of the results is performed using the relative error, standard deviation, correlation coefficient, and root-mean-square error (RMSE). Also, precise point positioning (PPP) is used to better evaluate the proposed model at test stations. The value of the correlation coefficient at the radiosonde station for the ANFIS and voxel is 0.90 and 0.87, respectively. Also, the minimum RMSE calculated for the ANFIS and voxel are 1.02 and 1.06 mm, respectively. In the PPP analysis, an improvement of about 4 mm is observed in the coordinates of the test stations using ANFIS. The results confirm the capability and high accuracy of the proposed model in determining the temporal and spatial variations of PWV.

Variations in solar, geomagnetic, and seismic activity cause disturbance in the ionosphere that can be monitored effectively using GPS observations. Total electron content (TEC) is an effective parameter to understand structure of ionosphere and investigate ionospheric variability by making use of GPS receivers. The aim of this study is to determine TEC variations caused by three magnetic storms (Kp ≥ 8) occurred between 2012‒2017 years over ISTA station. GNSS-TEC was computed for 20 days before and day of magnetic storm. After that, 27 quiet days were defined to detect ionospheric disturbance during storm. Using statistical method, averages of TEC values were computed to obtain upper and lower bounds. Firstly, (Kp = 8) magnetic storm occured on 09.03.2012 was investigated and positive disturbance (about 11 TECU) was determined. Secondly, (Kp = 8.3) magnetic storm occured on 22.06.2015 was analyzed and negative disturbance (about 6 TECU) was obtained during magnetic storm. Thirdly, (Kp = 8.3) magnetic storm occured on 08.09.017 was discussed and positive disturbance (about 4 TECU) was confirmed.

Many scientists from different disciplines have studied earthquakes for many years. As a result of these studies, it has been proposed that some changes take place in the ionosphere layer before, during or after earthquakes, and that the ionosphere should be monitored in earthquake prediction studies. This study investigates the changes in the ionosphere created by the earthquake with a magnitude of Mw=7.2 in the northwest of Lake Erçek, which is located to the north of the province of Van in Turkey on 23 October 2011 and at 13:41 local time (−3 UT) with the epicenter of 38.75∘ N, 43.36∘ E using the TEC values obtained by the global ionosphere models (GIMs) created by IONOLAB-TEC and CODE. In order to see whether the ionospheric changes obtained by the study in question were caused by the earthquake or not, the ionospheric conditions were studied by utilizing indices providing information on solar and geomagnetic activities (F10.7 cm, Kp, Dst). One of the results of the statistical test of the TEC values obtained from both models is positive and negative anomalies obtained for the times before, on the day of and after the earthquake, and the reasons for these anomalies are discussed in detail in the last section of the study. As the ionospheric conditions on the analyzed days were highly variable, it was thought that the anomalies were caused by geomagnetic effects, solar activity and the earthquake.

We propose a new method for spatio-temporal modeling of the ionospheric total electron content (TEC) at severe solar activity periods using a support vector machine (SVM). Using the observations from 37 GPS stations of the Iranian permanent GPS network (IPGN) in 2013–2014, the new model has been evaluated. Observations of 33 stations are used for training. Also, four stations are selected to test and evaluate the new model. In the 2 years under evaluation, the solar activity index is the maximum. The new model results are compared with the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) as other soft computing algorithms. Training of ANFIS and ANN is done with the error back-propagation (BP) method. The new model has been evaluated on different days, months, and seasons. To validate the results, the root mean square error (RMSE), correlation coefficients, residual histograms, relative errors, and standard deviations are used. Also, all the results are compared with the TEC of the global ionosphere map (GIM) as the traditional ionospheric model. For the SVM, GIM, ANFIS, and ANN models, the average RMSE at test stations are 3.642, 6.723, 4.844, and 5.011 TECU, respectively. Also, the average correlation coefficients of the four models evaluated at the test stations are calculated as 0.910, 0.742, 0.863, and 0.841, respectively. The maximum and minimum standard deviations for the SVM model obtained are 4.064 and 3.075 TECU, respectively. The standard deviations of the GIM, ANFIS, and ANN models are greater than the standard deviation of the SVM. Evaluations show that the SVM has high accuracy in modeling the temporal and spatial variations of the ionospheric TEC during periods of severe solar activity. Also, the analysis performed in precise point positioning (PPP) shows a higher accuracy of the new model compared to the other three models at the test stations. The results show that the SVM model is an accurate and reliable alternative to conventional ionospheric models for modeling the temporal and spatial variations of the TEC in the Iranian region.

Hassas ve güvenilir hız bilgilerinin elde edilmesi ve konum doğruluğu GNSS bazlı çok sayıda çalışma için önem arz etmektedir. Bu sebeple, nokta hızlarının kullanılabilirliği için gereken optimum sürenin belirlenmesi gerekmektedir. İç Anadolu Bölgesinin güneyinde yer alan ve deprem riskinin en az olduğu illerden biri olan Karaman ilinde bulunan TUSAGA-Aktif istasyonlarından biri olan KAMN istasyonuna ait 24 saatlik RINEX verileri 8 adet IGS istasyonuna dayalı olarak İsviçre’nin Bern üniversitesi tarafından geliştirilen Bernese v5.2 bilimsel GNSS yazılımı ile değerlendirilmiştir. KAMN istasyonunun 2016-2019 yılları arasında her yılın Ocak ve Haziran aylarının 1-18 günleri çalışma günleri olarak seçilmiştir. 1 Ocak 2016’dan başlanmış, birbirini takip eden 5’er günlük kombinasyonlara ayrılarak (1-5, 2-6, 3-7, …, 14-18) 14 farklı kombinasyon oluşturulmuştur. KAMN istasyonunun 30 sn RINEX verileri kullanılarak her kombinasyon için 6, 12, 18, 24, 30, 36 ve 42 aylık 7 farklı dönemde ITRF96 2005.00 epoğunda hız bileşenleri elde edilmiştir. Elde edilen hız bileşenleri, TKGM tarafından yayınlanan güncel hız bileşenleri ile karşılaştırılmıştır ve istatistik test yapılarak uyumlu olup olmadığı irdelenmiştir. Yapılan istatistik test sonucunda, VX ve VY kartezyen koordinat hızları için sonuçların 30 ay (2.5yıl)’lık dönemden, VZ için 36 ay (3yıl)’lık dönemden itibaren hesaplanan hızların, bilinen hızlarla uyumlu olduğu anlaşılmıştır. Zaman arttıkça elde edilecek olan hızların doğruluğu da artmaktadır. Bundan dolayı zaman kavramı önemli olup, jeodezik amaçlı çalışmalarda yorum yapabilmek için hız bileşenleri elde edilirken en az 36 ay (3 yıl)’lık bir veri uzunluğuna ihtiyaç olduğu sonucuna varılmıştır.

WOS:000610265200001 The purpose of the present study is to investigate simultaneously pre-earthquake ionospheric and atmospheric disturbances by the application of different methodologies, with the ultimate aim to detect their possible link with the impending seismic event. Three large earthquakes in Mexico are selected (8.2 Mw, 7.1 Mw and 6.6 Mw during 8 and 19 September 2017 and 21 January 2016 respectively), while ionospheric variations during the entire year 2017 prior to 37 earthquakes are also examined. In particular, Total Electron Content (TEC) retrieved from Global Navigation Satellite System (GNSS) networks and Atmospheric Chemical Potential (ACP) variations extracted from an atmospheric model are analyzed by performing statistical and spectral analysis on TEC measurements with the aid of Global Ionospheric Maps (GIMs), Ionospheric Precursor Mask (IPM) methodology and time series and regional maps of ACP. It is found that both large and short scale ionospheric anomalies occurring from few hours to a few days prior to the seismic events may be linked to the forthcoming events and most of them are nearly concurrent with atmospheric anomalies happening during the same day. This analysis also highlights that even in low-latitude areas it is possible to discern pre-earthquake ionospheric disturbances possibly linked with the imminent seismic events.

This paper presents the performance of the latest version of the International Reference Ionosphere (IRI-2016) model for estimating the Total Electron Content (TEC) variations in the mid-latitudinal Turkish regions during low solar activity years (2015–2018). Moreover, TEC is estimated from dual-frequency Global Navigation Satellite System (GNSS) receivers operating in different regions of Turkey: Zonguldak (41.44 °N, 31.77 °E), Ankara (39.88 °N, 32.75 °E) and Antalya (36.88 °N, 30.66 °E). The diurnal, monthly and seasonal variations in the measured TEC are compared with the prediction of modelled TEC. The results reveal minimum variations in the diurnal TEC after midnight hours 21:00–24:00 Universal Time ( $\text{UT}=\text{LT}+3{:}00~\text{hrs}$ ) and maximum TEC enhancement during the daytime from 9:00–15:00 UT (12:00–18:00 LT). The monthly mean TEC from measured and modelled estimation depicts the highest and lowest values in April and December during the years 2015–2018, respectively. Moreover, the low and high seasonal variations are observed in equinoxes and winter solstice, respectively. Furthermore, it is observed that IRI-2016 model underestimates the measured values, especially during the daytime (12:00–18:00 LT) at each station. The correlation coefficient between the measured and modelled TEC is found in the range ( $R=0.81\text{--}0.85$ ) during the years 2015–2018, which exhibits a high positive correlation over the regions. The model predicted TEC is showing a good agreement with measured TEC of the ionospheric dynamics over the regions, which manifests the radio signals accuracy over the mid-latitude regions, particularly Turkey.

This study aims to predict daily ionospheric Total Electron Content (TEC) using Gaussian Process Regression (GPR) model and Multiple Linear Regression (MLR). In this case, daily TEC values from 2015 to 2017 of two Global Navigation Satellite System (GNSS) stations were collected in Turkey. The performance of the GPR model was compared with the classical MLR model using Taylor diagrams and relative error graphs. Six models with various input parameters were performed for both GPR and MLR techniques. The results showed that although the models perform similarly, the GPR model estimated the TEC values more precisely at one and two days ahead. Therefore, the GPR model is recommended to forecast the TEC values at the corresponding GNSS stations over Turkey.

The analysis of the unexpected ionospheric phases before large earthquakes is a popular approach in earthquake prediction studies. In this study, the Total Electron Content (TEC) data of five International GNSS Service (IGS) stations and the Global Ionosphere Maps (GIMs) were used. The Short-time Fourier Transform (STFT) and a running median process were applied on the TEC time series to detect abnormalities before the Mw7.3 Iran-Iraq border earthquake on November 12, 2017. The analyzes showed positive anomalies 8–9 days before the earthquake and some positive/negative anomalies 1–6 days before the earthquake. These anomalies were cross-checked by space weather indices Kp, Dst, F10.7, Bz component of the interplanetary magnetic field (IMF Bz), electric field (Ey), and plasma speed (VSW). The results showed that the anomalies 1–6 days before the earthquake caused by a moderate magnetic storm. Also, the positive anomalies 8–9 days before the earthquake should be related to the Iran-Iraq border earthquake due to quiet space weather, local dispersion, and proximity to the epicenter.

Ionospheric total electron content (TEC) variations estimated from dual-frequency receivers of global positioning systems (GPS) can be used as a tool to detect possible seismo-ionospheric anomalies before strong earthquakes. In this study, possible seismo-ionospheric anomalies before the 2018 Papua New Guinea earthquake (6.0684°S, 142.7678°E, February 25, 2018, 00:44:43 UTC, Mw = 7.5) was investigated by using GPS-TEC variations from four International GNSS Service (IGS) stations (KAT1, TOW2, SOLO and ALIC) near the epicenter and four IGS station (USUD, SHAO, WIND and ZAMB) outside of the EPZ. The earthquake (EQ) is a local phenomenon and space weather conditions (SWCs) are a global phenomenon so that we selected four IGS stations away from the earthquake epicenter, which is at the same latitude and the same longitude with the earthquake to figure out a clear SWCs' effects on the GPS-TEC anomalies. In order to see these effects on ionospheric TEC variations, 10 different SWC indices were used. An analysis of occurrence time and duration of the following conclusions was made: both positive and negative anomalies are ensuing, and earthquake-related ionospheric anomalies revealed that they occurred 1 to 7 days before the associated earthquake. At the end of the study, the potential causes of these results were examined. Analysis results revealed that anomalies occurred, and the possible ionospheric GPS-TEC anomalies may be the earthquake precursors obtained 4 to 9 days before the Papua New Guinea earthquake. The increase in scientific research to analyze the seismo-ionospheric anomalies that occur before earthquakes will become an important warning tool to alert people to the devastating environmental effects of earthquakes.

Global positioning system (GPS) stations located along coastal areas have the ability to measure tide gauge records by reflected signal reception from the sea water surface. In this study we used the GPS signal-to-noise ratio data from the GPS station (44.6257 ⁰N, 124.0455 ⁰W) located at South Beach, Oregon, United States, to estimate the tide gauge records using seasonal measurements. First, we derived the tide gauge record from the station and used traditional tide gauge data from the National Water Level Observation Network (NWLON) sentinel station (Station ID: 9435380) located in Oregon for validation purposes because it was closest to the GPS station. Our results show that the GPS tide gauge and NWLON tide gauge correlate well with the correlation coefficient of 0.942 and the root mean square of their residuals was about 12.90 cm. The corresponding tide gauge prediction by auto-regressive moving average and singular spectrum analysis models are evaluated for their effectiveness over the station. The comparative analysis demonstrates that the GPS tide gauge has improved correlation with auto-regressive moving average predicted tide gauge (correlation coefficient of ~0.981 and root mean square of ~4.80 cm), and singular spectrum analysis predicted tide gauge (correlation coefficient of ~0.998 and root mean square of ~0.88 cm) compared to the NWLON tide gauge (correlation coefficient of ~0.942 and root mean square of ~12.90 cm) values. We believe the outcomes from this study contribute to a better understanding of the numerical modeling of tide gauge records as well as other measurements based on reflectometry techniques.

The ionosphere is a dynamic layer which generally changes according to radiation emitted by the sun, the movement of the earth around the sun, and sunspot activity. Variations can generally be categorized as regular or irregular variations. Both types of variation have a huge effect on radio wave propagation. In this study, we have focused on the seasonal variation effect, which is one of the regular forms of variation in terms of the ionosphere. We examined the seasonal variation over the ZONG station in Turkey for the year 2014. Our analysis results and IRI-2012 present different ideas about ionospheric activity. According to our analysed results, the standard deviation reached a maximum value in April 2014. However, the maximum standard deviation obtained from IRI-2012 was seen in February 2014. Furthermore, it is clear that IRI-2012 underestimated the VTEC values when compared to our results for all the months analysed. The main source of difference between the two models is the IRI-2012 topside ionospheric representation. IRI-2012 VTEC has been produced as a result of the integration of an electron density profile within altitudinal limits of 60–2000 km. In other words, the main problem with regard to the IRI-2012 VTEC representation is not being situated in the plasmaspheric part of the ionosphere. Therefore we propose that the plasmaspheric part should be taken into account to calculate the correct TEC values in mid-latitude regions, and we note that IRI-2012 does not supply precise TEC values for use in ionospheric studies.

The aim of this study was to model and predict seasonal ionospheric total electron content (TEC) using artificial neural network (ANN). Within this scope, GPS observations acquired from ANKR GPS station (Turkey) in 2015 were utilized to model TEC variations. Considering all data for each season, training and testing data were set as 80% and 10%, respectively, and the rest of the data were used to estimate TEC values using extracted mathematical models of ANN method. Day of Year (DOY), hour, F107 cm index (solar activity), Kp index and DsT index (magnetic storm index) were considered as the input parameters in ANN. The performances of ANN models were evaluated using RMSE and $R$ statistical metrics for each season. As a result of the analyses, considering the prediction results, ANN presented more successful predictions of TEC values in winter and autumn than summer and spring with RMSE 3.92 TECU and 3.97 TECU, respectively. On the other hand the $R$ value of winter data set (0.74) was lower than the autumn data set (0.88) while the RMSE values were opposite. This situation can be caused by the accuracy and precision of data sets. The results showed that the ANN model predicted GPS-TEC in a good agreement for ANKR station.

On August 11, 2012, two successive strong earthquakes (Mw = 6.4 and 6.3) hit the East Azerbaijan Province, NW Iran at 12:23 and 12:34 UTC. This study investigated the variations of multi earthquakes precursors, (Land Surface Temperature (LST), Total Electron Content (TEC), Outgoing Longwave Radiation (OLR), and Air Temperature (AT) of non-seismic phenomena before and after the successive Ahar (Iran) earthquakes. In this study, we used the moving median method to detect the anomalies in TEC, whereas ±2σ (95% confidence level) interval was used in MODIS LST and OLR analyses as an anomaly indicator. The results indicated that the MODIS Nighttime LST decreased by 5.5–6.3 °C compared to the mean (LST) within 10–12 days before the earthquakes Then a dramatic increase was observed on August 02, 2012. The LST results were consistent with the results of OLR, where the anomaly was observed on July 29, 2012. In addition to LST and OLR, significant TEC anomalous disturbances (increase and decrease) occurred two weeks before the earthquakes started on 28th July and reached the highest value 3 days before the earthquakes. Furthermore, AT analysis also provided identical results with LST behaviors. These results were instructive for seismologists and represent the importance of the corresponding potential precursors for upcoming earthquakes.

In this case study, an active runway of a civilian airport in Zonguldak, Turkey was used to assess the suitability of spaceborne digital elevation models (DEMs) to model an anthropogenic structure. The tested DEMs include the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30), the Shuttle Radar Topography Mission (SRTM)-1&rdquo, the SRTM-3&rdquo, the SRTM-X, the TanDEM-3&rdquo, and the WorldDEMTM. A photogrammetric high accuracy DEM was also available for the tests. As a reference dataset, a line-leveling survey of the runway using a Leica Sprinter 150/150M instrument was performed. The selection of a runway as a testbed for this type of investigation is justified by its unique characteristics, including its flat surface, homogenous surface material, and availability for a ground survey. These characteristics are significant because DEMs over similar structures are free from environment- and target-induced error sources. For our test area, the most accurate DEM was the WorldDEMTM followed by the SRTM-3&rdquo, and TanDEM-3&rdquo, with vertical errors (LE90) equal to 1.291 m, 1.542 m, and 1.56 m, respectively. This investigation uses a method, known as the runway method, for identifying the vertical errors in DEMs.

This study aims to investigate the behaviors of the potential earthquake precursors such as Total Electron Content (TEC), daytime/nighttime Land Surface Temperature (LST) and aerosols before and after the 2013 Mw 7.7 Awaran (Pakistan) earthquake. TEC values were obtained from Global Positioning System (GPS) measurements, and LST and aerosol values were retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites, respectively. In order to detect the anomalies, moving median method was used in TEC analyses and ±2σ (95% confidence level) interval was utilized in LST and Aerosol Optical Depth (AOD) analyses as anomaly indicator. TEC anomalies were identified 3 days (21.09.2013) and 5 days (19.09.2013) before the earthquake. In daytime/nighttime LST analyses, three regions of interest for spatial analyses were determined including epicenter, 10 km impact area around the epicenter and Modified Mercalli Intensity (MMI) 7.5 contour which was presented in the United States Geological Survey (USGS)'s shake map. Different anomalous days of LST were observed from these three regions of interest. In AOD analysis, only MMI 7.5 contour was considered due to its low spatial resolution (10 × 10km pixel size). The anomaly in AOD values was observed on 29.09.2013, 5 days after the earthquake. However, the abnormal behavior of AOD on 21.09.2013 was consistent with GPS-TEC anomaly. Furthermore, considering daytime/nighttime LST values on epicenter and 10 km impact area, we observed a systematic increase culminated near the earthquake event and a decrease after the earthquake. On the contrary, a systematic decrease and then increase of the AOD values were identified. The obtained results showed that TEC, LST and AOD were important potential precursors for Earthquake prediction; however, further developments on methods, data types and statistical analyses are still required.

Several scientists from different disciplines have studied earthquakes for many years. As a result of these studies, it has been proposed that some changes take place in the ionosphere layer before, during or after earthquakes, and the ionosphere should be monitored in earthquake prediction studies. This study investigates the changes in the ionosphere created by the earthquake with magnitude of Mw=7.2 in the northwest of the Lake Erçek which is located to the north of the province of Van in Turkey on 23 October 2011 and at 1.41 pm local time (−3 UT) with the epicenter of 38.758° N, 43.360° E using the TEC values obtained by the Global Ionosphere Models (GIM) created by IONOLAB-TEC and CODE. In order to see whether the ionospheric changes obtained by the study in question were caused by the earthquake or not, the ionospheric conditions were studied by utilizing indices providing information on solar and geomagnetic activities (F10.7 cm, Kp, Dst). As a result of the statistical test on the TEC values obtained from the both models, positive and negative anomalies were obtained for the times before, on the day of and after the earthquake, and the reasons for these anomalies are discussed in detail in the last section of the study. As the ionospheric conditions in the analyzed days were highly vibrant, it was thought that the anomalies were caused by geomagnetic effects, solar activity and the earthquake. The authors believe that interdisciplinary studies are needed to distinguish the earthquake-related part of the anomalies in question.

Many scientists from different disciplines have studied earthquakes for many years. As a result of these studies, it has been proposed that some changes take place in the ionosphere layer before, during or after earthquakes, and the ionosphere should be monitored in earthquake prediction studies. This study investigates the changes in the ionosphere created by the earthquake with magnitude of Mw=7.2 in the northwest of the Lake Erçek which is located to the north of the province of Van in Turkey on 23 October 2011 and at 1.41 pm local time (−3 UT) with the epicenter of 38.75° N, 43.36° E using the TEC values obtained by the Global Ionosphere Models (GIM) created by IONOLAB-TEC and CODE. In order to see whether the ionospheric changes obtained by the study in question were caused by the earthquake or not, the ionospheric conditions were studied by utilizing indices providing information on solar and geomagnetic activities (F10.7 cm, Kp, Dst). One of the results of the statistical test on the TEC values obtained from the both models, positive and negative anomalies were obtained for the times before, on the day of and after the earthquake, and the reasons for these anomalies are discussed in detail in the last section of the study. As the ionospheric conditions in the analyzed days were highly variable, it was thought that the anomalies were caused by geomagnetic effects, solar activity and the earthquake.

International Conference on Sensors and Models in Remote Sensing and Photogrammetry -- NOV 23-25, 2015 -- Kish Island, IRAN, WOS: 000380618200060, Ionosphere has been studied by a number of scientists in recent years. Since GPS observations cannot provide TEC value directly, it can be estimated from combination of observations. In this study TEC values derived from GPS observations were produced variations with two hours increments from the eighth day to fifteenth day in months just after beginning of each season, namely January, April, July and October in 2014 for ZONG TUSAGA-Aktif station and 41 other stations (TUSAGA-Aktif, EUREF, IGS). TEC values computed by the Bernese 5.0 software were compared with the Global Ionosphere Model (GIM) TEC values regularly published by Center for Orbit Determination in Europe (CODE) and International Reference Ionosphere (IRI-2012) TEC values. As a result of this study, with the comparison of GPS TEC values to GIM TEC values, it has become obvious that GPS TEC values are quite similar to the GIM TEC values. The differences of TEC values derived from GPS TEC and GIM TEC change from 0.91 TECU (January) to 1.88 TECU (October). On the other hand, GPS TEC values are also compared with IRI TEC values, and found out that there is a considerable difference between the two TEC values ranging from 6.30 TECU (January) to 15.15 TECU (April). Moreover, TEC values derived from GPS measurements are attained similar to GIM TEC, but found to stray from IRI-2012 TEC values remarkably.

This study used the Unmanned Aerial Vehicle (UAV), which was designed and produced to monitor rapidly occurring landslides in forest areas. It was aimed to determine the location data for the study area using image sensors integrated into the UAV. The study area was determined as the landslide sites located in the Taşlıçiftlik Campus of Gaziosmanpaşa University, Turkey. It was determined that landslide activities were on going in the determined study area and data was collected regarding the displacement of materials. Additionally, it was observed that data about landslides may be collected in a fast and sensitive way using UAVs, and this method is proposed as a new approach. Flights took place over a total of five different periods. In order to determine the direction and coordinate variables for the developed model, eight Ground Control Points (GCPs), whose coordinates were obtained with the GNSS method, were placed on the study area. In each period, approximately 190 photographs were investigated. The photos obtained were analysed using the PIX4D software. At the end of each period, the RMS and Ground Sample Distance (GSD) values of the GCPs were calculated. Orthomosaic and Digital Surface Models (DSM) were produced for the location and height model. The results showed that max RMS = ±3.3 cm and max GSD = 3.57 cm/1.40 in. When the first and fifth periods are compared; the highest spatial displacement value ΔS = 111.0 cm, the highest subsidence value Δh = 37.3 cm and the highest swelling value Δh = 28.6 cm as measured.

This study used an unmanned aerial vehicle (UAV) that was designed and produced to monitor rapidly occurring landslides in forest areas. It aimed to determine the location data for the study area using image sensors integrated into the UAV. The study area was determined as the landslide sites located in the Taşlıçiftlik campus of Gaziosmanpaşa University, Turkey. It was determined that landslide activities were on going in the determined study area and data was collected regarding the displacement of materials. Additionally, it was observed that data about landslides may be collected in a fast and sensitive way using UAVs, and this method is proposed as a new approach. Flights took place over a total of five different periods. In order to determine the direction and coordinate variables for the developed model, eight Ground Control Points (GCPs), whose coordinates were obtained using the GNSS method, were placed on the study area. In each period, approximately 190 photographs were investigated. The photos obtained were analyzed using the Pix4D software. At the end of each period, the Root Mean Square and Ground Sample Distance (GSD) values of the GCPs were calculated. Orthomosaic and digital surface models (DSM) were produced for the location and height model. The results showed that max RMS=±3.3 cm and max GSD=3.57 cm. When the first and fifth periods were compared, the highest spatial displacement value ΔS=111.0 cm, the highest subsidence value Δh=37.3 cm and the highest swelling value Δh=28.6 cm were measured., U ovom radu korištena je bespilotna letjelica (UAV) koja je dizajnirana i proizvedena za potrebe praćenja nagle pojave klizišta u šumskim područjima. Njezin cilj je određivanje podataka o lokaciji područja istraživanja koristeći slikovne senzore ugrađene u UAV. Područje istraživanja je lokacija klizišta u kampusu Taşlıçiftlik Sveučilišta Gaziosmanpaşa, Turska. Utvrđeno je da se klizišta stalno pojavljuju u području istraživanja, a podaci su prikupljeni na temelju pomaka materijala. Osim toga, ustanovljeno je da se podaci o klizištima mogu prikupljati brzo i precizno koristeći UAV, te je ta metoda predložena kao novi pristup. Letovi su obavljeni kroz pet različitih razdoblja. Kako bi se utvrdile varijable smjera i koordinata za razvijeni model, na području istraživanja, postavljeno je osam stalnih točaka (GCP) čije su koordinate dobivene GNSS metodom. U svakom razdoblju proučeno je otprilike 190 fotografija. Dobivene fotografije su analizirane pomoću softvera Pix4D. Na kraju svakog razdoblja izračunate su vrijednosti Root Mean Square (RMS) i Ground Sample Distance (GSD) za GCP. Ortomozaični i digitalni modeli površine (DSM) izrađeni su za lokacijski i visinski model. Rezultati su pokazali da je maksimalni RMS=±3,3 cm, a maksimalni GSD=3,57 cm. Nakon usporedbe prvog i petog razdoblja, izmjerene su najviša vrijednost prostornog pomaka ΔS=111,0 cm, najviša vrijednost slijeganja Δh=37,3 cm i najviša vrijednost ispupčenosti Δh=28,6 cm.

Turkey is a country located in the middle latitude zone, where tectonic activity is intensive. Recently, an earthquake of magnitude 6.5 Mw occurred offshore in the Aegean Sea on 24 May 2014 at 09:25 UTC, which lasted about 40 s. The earthquake was also felt in Greece, Romania, and Bulgaria in addition to Turkey. In recent years, ionospheric anomaly detection studies have been carried out because of seismicity with total electron content (TEC) computed from the global navigation satellite system's (GNSS) signal delays and several interesting findings have been published. In this study, both TEC and positional variations have been examined separately following a moderate size earthquake in the Aegean Sea. The correlation of the aforementioned ionospheric variation with the positional variation has also been investigated. For this purpose, a total of 15 stations was used, including four continuously operating reference stations in Turkey (CORS-TR) and stations in the seismic zone (AYVL, CANA, IPSA, and YENC), as well as international GNSS service (IGS) and European reference frame permanent network (EPN) stations. The ionospheric and positional variations of the AYVL, CANA, IPSA, and YENC stations were examined using Bernese v5.0 software. When the precise point positioning TEC (PPP-TEC) values were examined, it was observed that the TEC values were approximately 4 TECU (total electron content unit) above the upper-limit TEC value at four stations located in Turkey, 3 days before the earthquake at 08:00 and 10:00 UTC. At the same stations, on the day before the earthquake at 06:00, 08:00, and 10:00 UTC, the TEC values were approximately 5 TECU below the lower-limit TEC value. The global ionosphere model TEC (GIM-TEC) values published by the Centre for Orbit Determination in Europe (CODE) were also examined. Three days before the earthquake, at all stations, it was observed that the TEC values in the time period between 08:00 and 10:00 UTC were approximately 2 TECU above the upper-limit TEC value; 1 day before the earthquake at 06:00, 08:00, and 10:00 UTC, the TEC values were approximately 4 TECU below the lower-limit TEC value. Again, by using the same 15 stations, positional variation investigation for before and after the earthquake was undertaken for the AYVL, CANA, IPSA, and YENC stations. As a result of the conducted analysis, positional displacements were seen before and after the earthquake at the CANA station, which is the nearest station to the earthquake centre. Before and after the earthquake, positional displacements were observed as 10 and 3 cm respectively.