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Intelligent health monitoring in 6G networks: machine learning-enhanced VLC-based medical body sensor networks
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-05-23) Antaki, Bilal; Dalloul, Ahmed Hany; Miramirkhani, Farshad
Recent advances in Artificial Intelligence (AI)-driven wireless communication are driving the adoption of Sixth Generation (6G) technologies in crucial environments such as hospitals. Visible Light Communication (VLC) leverages existing lighting infrastructure to deliver high data rates while mitigating electromagnetic interference (EMI); however, patient movement induces fluctuating signal strength and dynamic channel conditions. In this paper, we present a novel integration of site-specific ray tracing and machine learning (ML) for VLC-enabled Medical Body Sensor Networks (MBSNs) channel modeling in distinct hospital settings. First, we introduce a Q-learning-based adaptive modulation scheme that meets target symbol error rates (SERs) in real time without prior environmental information. Second, we develop a Long Short-Term Memory (LSTM)-based estimator for path loss and Root Mean Square (RMS) delay spread under dynamic hospital conditions. To our knowledge, this is the first study combining ray-traced channel impulse response modeling (CIR) with ML techniques in hospital scenarios. The simulation results demonstrate that the Q-learning method consistently achieves SERs with a spectral efficiency (SE) lower than optimal near the threshold. Furthermore, LSTM estimation shows that D1 has the highest Root Mean Square Error (RMSE) for path loss (1.6797 dB) and RMS delay spread (1.0567 ns) in the Intensive Care Unit (ICU) ward, whereas D3 exhibits the highest RMSE for path loss (1.0652 dB) and RMS delay spread (0.7657 ns) in the Family-Type Patient Rooms (FTPRs) scenario, demonstrating high estimation accuracy under realistic conditions.
Sustainable soil stabilization using colemanite: experimental and numerical analysis of sandy soils for improved geotechnical properties
(Springer Nature, 2025-06-12) Koçak Dinç, Beste; Dehghanian, Kaveh; Etminan, Ehsan
This paper discusses the use of colemanite, a boron compound, which is a natural additive to geotechnically improved sandy soils, thus providing an eco-friendly alternative to conventional soil stabilization. Clean angular sand was the base material with the addition of colemanite in amounts of 0%, 5%, 10%, and 15% by dry mass. Various laboratory tests, such as Atterberg limits, void ratio, specific gravity, compaction, permeability, and unconsolidated undrained triaxial tests, were carried out to determine the physical and mechanical characteristics of the produced mixtures. Numerical modeling, adopted by the PLAXIS finite element program, was used to carry out simulations under various conditions for soil profiles to determine and compare soil behavior. The findings revealed that the addition of colemanite significantly reduced permeability and void ratios while enhancing stiffness and strength, with 15% colemanite yielding the best performance. This study is one of those that focuses on the introduction of colemanite, which can also act as an effective stabilizer and is a much greener and more environmentally friendly option. Apart from this, it has other advantages both economically and ecologically by reducing the amount of cement, which is a high carbon source required for building based on this. The discoveries bring in the further development of green geotechnical engineering, which also includes the construction of sustainable infrastructures.
Retinal disease diagnosis in OCT scans using a foundational model
(Springer Science and Business Media Deutschland GmbH, 2025) Nazlı, Muhammet Serdar; Turkan, Yasemin; Tek, Faik Boray; Toslak, Devrim; Bulut, Mehmet; Arpacı, Fatih; Öcal, Mevlüt Celal
This study examines the feasibility and performance of using single OCT slices from the OCTA-500 dataset to classify DR (Diabetic Retinopathy) and AMD (Age-Related Macular Degeneration) with a pre-trained transformer-based model (RETFound). The experiments revealed the effective adaptation capability of the pretrained model to the retinal disease classification problem. We further explored the impact of using different slices from the OCT volume, assessing the sensitivity of the results to the choice of a single slice (e.g., “middle slice”) and whether analyzing both horizontal and vertical cross-sectional slices could improve outcomes. However, deep neural networks are complex systems that do not indicate directly whether they have learned and generalized the disease appearance as human experts do. The original dataset lacked disease localization annotations. Therefore, we collected new disease classification and localization annotations from independent experts for a subset of OCTA-500 images. We compared RETFound’s explainability-based localization outputs with these newly collected annotations and found that the region attributions aligned well with the expert annotations. Additionally, we assessed the agreement and variability between experts and RETFound in classifying disease conditions. The Kappa values, ranging from 0.35 to 0.69, indicated moderate agreement among experts and between the experts and the model. The transformer-based RETFound model using single or multiple OCT slices, is an efficient approach to diagnosing AMD and DR.
Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: a survey-based analysis
(Institute of Electrical and Electronics Engineers Inc., 2025-05-19) Kubilay, Burak; Çeliktaş, Barış
The rapid development of digital technology across industries has highlighted the growing need for enhanced competencies in Artificial Intelligence (AI), Cyber security (CS), and Digital Transformation (DT). While there is extensive research on each of these domains in isolation, few studies have investigated their relationship and joint impact on organizational maturity. This study aims to address this gap by analyzing the relationships among the maturity levels of AI, CS, and DT at the organizational level using Structural Equation Modeling (SEM) and descriptive statistical methods. A mixed-methods design combines quantitative survey data with synthetic modeling techniques to assess organizational preparedness. The findings demonstrate significant bidirectional correlations among AI, CS, and DT, with technology and finance being more advanced than government and education. The research highlights the necessity of an integrated AI-CS strategy and provides actionable recommendations to increase investments in these domains. In contrast to the preceding fragmented evaluations, the current research establishes a comprehensive, empirically grounded framework that acts as a strategic reference point for digital resilience. Follow-up studies will involve collecting real-world industry data in support of empirical validation and predictive ability in measuring AI and CS maturity. This research adds to the existing literature by filling the gaps among fragmented digital maturity models and providing a consistent empirical base for organizations to thrive in an evolving technological environment.
Goal-Oriented Random Access (GORA)
(Institute of Electrical and Electronics Engineers Inc., 2025-08) Topbaş, Ahsen; Ari, Çağrı; Kaya, Onur; Uysal, Elif
We propose Goal-Oriented Random Access (GORA), where transmitters jointly optimize what to send and when to access the shared channel to a common access point, considering the ultimate goal of the information transfer at its final destination. This goal is captured by an objective function, which is expressed as a general (not necessarily monotonic) function of the Age of Information. Our findings reveal that, under certain conditions, it may be desirable for transmitters to delay channel access intentionally and, when accessing the channel, transmit aged samples to reach a specific goal at the receiver.
Drought analysis based on nonparametric multivariate standardized drought index in the Seyhan River Basin
(Springer Science and Business Media B.V., 2025-05) Terzi, Tolga Barış; Önöz, Bihrat
Drought is a detrimental natural hazard that is a threat to the social and ecological aspects of life. Unlike other natural hazards, drought occurs slowly and gradually, making it difficult to detect its formation, leading to severe consequences in the affected area. Therefore, precise and reliable monitoring of drought is crucial to implement effective drought mitigation strategies. Drought indices are significant tools for drought monitoring; single variable indices are quite frequently used in the literature to assess drought conditions. Although these indices are generally accurate at characterizing the specific type of drought they were developed for, they fail to provide a comprehensive representation of drought conditions. Hence, this study applies a nonparametric multivariate standardized drought index (MSDI) that integrates meteorological and hydrological drought to investigate the dynamics of drought events within the Seyhan River Basin (SRB). Trend analyses were conducted to detect any directional changes in the drought patterns within the SRB. Additionally, this study examined the potential effects of El Nino-Southern Oscillation events on the MSDI series to determine their impact on drought conditions in the SRB. The results indicate that the MSDI outperforms the single variable indices in characterizing drought conditions within the basin. The calculations conducted for 5 different time scales 1, 3, 6, 9 and 12-months showed satisfactory results in multivariate analysis of drought. Upon examining the trend analyses, MSDI series showed an insignificant negative trend in all stations within the SRB. The MSDI series was strongly influenced by Nino 3.4 and Arctic Oscillation (AO) indices while sunspot activities had a relatively weak impact on the MSDI series.
Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words
(Institute of Electrical and Electronics Engineers Inc., 2025) Ercan, Gökhan; Yıldız, Olcay Taner
We show that n-gram-based distributional models fail to distinguish unrelated words due to the noise in semantic spaces. This issue remains hidden in conventional benchmarks but becomes more pronounced when orthographic similarity is high. To highlight this problem, we introduce OSimUnr, a dataset of nearly one million English and Turkish word-pairs that are orthographically similar but semantically unrelated (e.g., grammar - crammer). These pairs are generated through a graph-based WordNet approach and morphological resources. We define two evaluation tasks - unrelatedness identification and relatedness classification - to test semantic models. Our experiments reveal that FastText, with default n-gram segmentation, performs poorly (below 5% accuracy) in identifying unrelated words. However, morphological segmentation overcomes this issue, boosting accuracy to 68% (English) and 71% (Turkish) without compromising performance on standard benchmarks (RareWords, MTurk771, MEN, AnlamVer). Furthermore, our results suggest that even state-of-the-art LLMs, including Llama 3.3 and GPT-4o-mini, may exhibit noise in their semantic spaces, particularly in highly synthetic languages such as Turkish. To ensure dataset quality, we leverage WordNet, MorphoLex, and NLTK, covering fully derivational morphology supporting atomic roots (e.g., '-co_here+ance+y' for 'coherency'), with 405 affixes in Turkish and 467 in English.
Cognitive reserve and aging: impacts on theory of mind and executive functions
(Routledge, 2025-03) Şandor, Serra; Hıdıroğlu Ongun, Ceren; Yıldırım, Elif
Aim: This study examines the effects of cognitive reserve (CR) on Executive Functions (EF) and Theory of Mind (ToM). While CR is suggested to mitigate age-related cognitive decline, its relationship with social cognition remains limited and inconsistent in the literature. It was hypothesized that the effect of CR on ToM might be indirect, mediated by EF and working memory. Methods: 225 cognitively healthy participants were included. CR was measured with the Cognitive Reserve Index Questionnaire, EF with verbal fluency and the Stroop Test, and WM using digit span tasks. Structural Equation Modeling was used to analyze the relationships among CR, EF, WM, and SC, controlling for age and gender. Results: CR was significantly associated with both RMET and FPRT performances. Mediation analysis revealed the direct effects of CR on RMET performance, while the effects on FPRT performance were mediated by executive functions. WM had a partial mediating effect on EF and ToM, but did not directly influence FPRT. Education was most strongly associated with RMET performance, while leisure activities were linked to FPRT performance. Conclusion: These findings suggest that CR indirectly supports ToM by enhancing EF and highlight the importance of interventions aimed at strengthening executive control to support social cognition in aging.
Exploring the impact of Flash technique on test anxiety among adolescents
(SAGE Publications Ltd, 2025-07) Çitil Akyol, Canan; İnci İzmir, Sevim Berrin
This study aims to investigate the specific effects of Flash Technique (FT) on adolescents with test anxiety. This follow-up study consists of 38 adolescents, 14–17 years of age (M = 15.39, SD = 1.13). Pre-post assessments were conducted using the Test Anxiety Inventory (TAI), Scale of Attitudes Negatively Affecting the Performance I/Test (POET), and Beck Anxiety Inventory (BAI) at baseline, at the end of the 4thand 12thweeks of therapy. The FT was applied for 12 weeks, with one weekly session as an intervention. As a result of the therapy process, the baseline means of total BAI scores decreased from 25.26 to 2.18; the baseline means of TAI decreased from 149.79 to 39.13, and the baseline mean of POET decreased from 298.47 to 73.84 at the end of the 12th week of therapy. Also, the baseline means of SUD scores decreased from 9.42 to zero at the end of the 12th week of treatment. All the adolescents showed complete improvement after the 12th week of the FT. The study findings showed that the test anxiety symptoms significantly decreased with the treatment of the FT. FT can be an effective intervention for test anxiety in adolescents.
Turkey and Ukraine’s strategic positioning: economic, energy, and military cooperation in the geopolitical landscape between the European Union and Russia
(Taylor and Francis, 2025-01-01) Karakaya Polat, Rabia; Lebduška, Michal
The geopolitical positioning of Ukraine and Turkey between the European Union (EU) and Russia holds significant implications for their economies. The analysis first highlights how Turkey’s balancing act between the West and Russia has been further complicated by the war in Ukraine, offering both challenges and opportunities in economic, energy, and military fields. The chapter then turns to Ukraine showing how the annexation of the Crimea and military intervention in Donbas in 2014 prompted extensive internal reforms and a definitive shift towards European and Euro-Atlantic integration. The chapter argues that the domestic political trajectories in Turkey and Ukraine involve sequences of opening and closing, democratic reform and authoritarian resurgence, influenced by internal factors but simultaneously linked to the EU’s ambiguity towards their European aspirations and Russia’s geopolitical aspirations.
Treatment and long-term outcome of mental disorders: The grim picture from a quasi-epidemiological investigation in 54,826 subjects from 40 countries
(Elsevier Ireland Ltd, 2025-06) Fountoulakis, Konstantinos N.; Karakatsoulis, Gregory; Abraham, Seri; Adorjan, Kristina; Uddin Ahmed, Helal; Alarcòn, Renato Daniel; Arai, Kiyomi; Auwal, Sani Salihu; Berk, Michael; Levaj, Sarah; Yılmaz Kafalı, Helin
Introduction: This study registered rates of specific treatment options for mental disorders as well as their long-term outcome. Material and methods: The history of mental disorders was used as a proxy for diagnosis. The data came from the COMET-G study (40 countries; 54,826 subjects, 64.73 % females, 35.45±13.51 years old). The analysis included descriptive statistics, Risk Ratios, t-tests, and ANCOVA's. Results: 24.14 % reported a history of any mental disorder (depression >12 %, non-affective psychosis and Bipolar disorder 1 % each, >20 % self-injury, >10 % had attempted suicide, 7.17 % illegal substance abuse). Most patients were not under any kind of treatment (59.44 %) and most were not receiving treatment as recommended (e.g. 90 % of Bipolar and 2/3 of psychotic patients). No treatment at all and psychotherapy as monotherapy were consistently related to poorer outcomes. In anxiety or depression, only antidepressant monotherapy and benzodiazepines, in Bipolar disorder only antipsychotic monotherapy in males and antidepressant monotherapy in females and in non-affective psychosis antipsychotics and psychotherapy in females only, were related to good outcomes. No treatment modality was related to a good outcome in those with a history of self-harm, suicidal attempts, or illegal substance use. Only depression and treatment with antidepressants were related to metabolic syndrome. Discussion: In the community, the overwhelming majority of mental patients do not receive appropriate treatment or, even worse, no treatment at all. The outcome is unfavourable for the majority and only a few selective treatment options seem to make a difference.
Decision making, emotion recognition and childhood traumatic experiences in murder convicts ımprisoned with aggravated life sentence: a prison study
(Turkish Neuropsychiatric Society, 2025-03) Çıkrıkçılı, Uğur; Yıldırım, Elif; Buker, Seda; Ger, Can; Erözden, Ozan; Gürvit, Hakan; Saydam, Bilgin
Introduction: Decision-making and emotion recognition are two fundamental themes in social cognition. Disorders in these areas can lead to interpersonal, psychosocial, and legal problems for the individual and society. The likelihood of consequent aggression and crime makes them foci of forensic psychiatry over time. In this study, two developmental disorders that have a clear relationship with crime, that are antisocial personality disorder (ASPD), and psychopathy are investigated for their relationship with these social cognitive deficits.Methods: The present study involved 23 male prison inmates who were diagnosed with both antisocial personality disorder and psychopathy, as well as 23 control participants who were matched for age, gender, and level of education. Following the psychiatric interview, Reading the Mind in the Eyes Test (RMET), the Iowa Gambling Test (IGT), Toronto Alexithymia Scale (TAS), Defense Styles Questionnaire (DSQ), Childhood Psychic Trauma Scale (CTQ), Hare Psychopathy Checklist (PCL-R) were administered to all participants. Results: The results of the study showed that ASPD group performed statistically worse than healthy controls in TAS, CTQ, all items of DSQ, PCL-R Factor 1 and 2, and all the IGT scores (p<0.05). There were no statistically significant difference between in the RMET test performancesConclusion: These results suggest that ASPD and psychopathy lead to impaired decision-making behaviors due to the inability to recognize one’s own emotions and impulsivity, and that these characteristics play a critical role in the criminal behavior of individuals. In addition, contrary to expectations, the results of affective theory of mind assessed with the RMET showed similar characteristics in homicide convicts and healthy controls. These data indicate the need for further research in the field of forensic psychiatry.
Design and control of high-frequency buck converter fed six-step drive for air-core PMSM
(Institute of Electrical and Electronics Engineers Inc., 2025-02) Jena, Sritam; Kumar, Saurabh; Deshmukh, Akshay Vijayrao; Hava, Ahmet Masum; Akın, Bilal; Gabrys, Christopher; Rodgers, Timothy
Air-core permanent magnet synchronous motors (PMSMs) machines are becoming known for their higher efficiency, lighter weight designs, and superior performance compared to widely utilized induction motors (IMs). They hold great potential for diverse industrial applications. However, effectively harnessing this potential requires overcoming drive hardware and control challenges. This research introduces a silicon carbide (SiC)-based two-phase interleaved buck-converter-fed quasi-current source inverter (quasi-CSI) drive tailored for driving low-inductance air-core PMSMs which is ideal for heavy-duty fan and pump applications. Operating in the discontinuous current mode (DCM) with an effective switching frequency of 1 MHz, this drive is designed to address efficiency and the very low-cost market constraints while simultaneously reducing control complexity an issue associated with its high switching frequency. The article also analyzes two critical control challenges of mitigating high current spikes due to air-core machines' low inductance and finding solutions to overcome microcontroller resource limitations when executing time-critical functions within interrupt subroutines (ISRs). The culmination of this work is a 300 V dc-bus and five-horsepower electric drive prototype with closed-loop speed control. Experimental results illustrate a 2% enhancement in overall efficiency compared to conventional induction machine (IM) drives in similar applications (e.g., fan and pump) and ratings, alongside a significant 50% reduction in drive volume.
A practical control method for single-phase input PMSM drives with small DC-Link capacitor
(Institute of Electrical and Electronics Engineers Inc., 2025-03) Deshmukh, Akshay Vijayrao; Afshar, Mojtaba; Jena, Sritam; Hava, Ahmet Masum; Yu, Zhen; Akın, Bilal
The primary function of the large electrolytic dc-link capacitor in the single-phase input motor drives is to enhance dc voltage stability by minimizing voltage fluctuations. Because they are both bulky and unreliable components, reducing the size of dc-link capacitors or substituting them with significantly low capacitance value film capacitors offers numerous advantages. This article introduces a method that allows using a voltage source inverter with a small film dc-link capacitor, which results in equivalent performance to high-value capacitor drives. In this approach, the effect of dc-link voltage ripple is designed as a periodic disturbance in the current loop. A proportional-integral-resonant (PIR) control strategy is implemented to eliminate the designed double-line frequency disturbance observed in motor currents, thereby ensuring a smooth motor torque response. This solution meets crucial criteria for home appliances, specifically addressing input current harmonic requirements through power factor correction while effectively reducing the adverse effects of substantial dc-link ripple on motor torque, even when employing a film capacitor (10–50 µF range). The proposed framework is experimentally tested on permanent magnet synchronous motors with fan-load and dynamometers. Experimental results demonstrate, with PIR, an 80% reduction in current and torque ripple occurring due to the use of a low-value dc-link film capacitor. This also achieves performance within ±5% of the results obtained with a drive equipped with a 1200 µF dc capacitor. In addition, the line-side power factor exceeds 0.98 for loads exceeding 8% of the rated power.
Emotion dysregulation as a mediator between parental emotional availability and game addiction among Turkish late adolescents
(Routledge, 2025-02) Ülkümen, İpek; Aktan, Zekeriya Deniz
This study aimed to examine how difficulty in emotion regulation mediates the relationship between parental emotional availability and game addiction among Turkish late adolescents. 537 adolescents completed the Lum Emotional Availability of Parents, the Difficulties in Emotion Regulation Scale, Internet Gaming Disorder Short Form, and Sociodemographic Characteristics and Data Form. The results show that difficulty in emotion regulation fully mediates between parental emotional availability scores and adolescents’ game addiction scores. It can be concluded that interventions for the prevention and treatment of game addiction in late adolescents should focus on improving the parents’ emotional support and adolescents’ emotion regulation skills.
Turkish validity and reliability study of the childhood illness attitude scale
(Routledge, 2025-03) Aktan, Zekeriya Deniz; İnci İzmir, Sevim Berrin; Ünlü, Beyza; Yılmaz Kahraman, İpek Su
Severe forms of health anxiety cause serious dysfunction in people’s lives. Childhood Illness Attitude Scales (CIAS) is an assessment tool used to evaluate childhood health anxiety yet has not been validated for use in Turkey. The study aimed to examine the psychometric properties and factor structure of the Turkish version of the CIAS (CIAS-TR). The scale was administered to 306 children aged between 8 and 15 years. In addition to the CIAS-TR, participants were asked to complete the Screen for Child Anxiety-Related Emotional Disorders (SCARED) and the Pediatric Quality of Life Inventory (PedsQL). To measure test-retest reliability, CIAS-TR was completed by participants 15 days later. Results demonstrated good psychometric properties with high internal consistency and test-retest reliability. A positive correlation with SCARED and a negative correlation with PedsQL. Results from Confirmatory Factor Analysis suggested that a four-factor model best fit the data. The findings of the study indicate that the Turkish adaptation of the CIAS is an appropriate tool for assessing health anxiety in children.
A new frontier in design studio: AI and human collaboration in conceptual design
(KeAi Communications Co., 2025) Karadağ, Derya; Ozar, Betül
This study explores the role of artificial intelligence (AI) in the conceptual design phase of interior design education, focusing on AI's potential to help students visualise and refine creative ideas. Conducted within a design studio course, the research integrates text-to-image generators, particularly Midjourney to support students' design processes. Implemented in the fourth week of a 14-week course, a structured workshop introduced students to Midjourney, with surveys conducted both at this stage and during the final submission to capture changes in student perspectives. Using a two-phase case study involving a workshop, surveys, and interviews among senior undergraduate students in the bachelor's program of the Interior Architecture and Environmental Design Department, the study assesses the impact of AI prompts, from simple keywords to detailed narratives, on concept development and project outcomes. Findings indicate that AI broadens design possibilities, facilitates iterative ideation, and improves conceptual precision through high-fidelity visualizations. While students view AI as a valuable addition to their creative process, they also express concerns about ethics and the need to balance AI's benefits with preserving design authenticity. This research contributes to the broader discussion on AI's role in design, advocating for a balanced integration that respects both technological potential and human creativity.
Critical digital data enabling traceability for smart honey value chains
(Taylor and Francis Ltd., 2025-02) Ziemba, Ewa Wanda; Maruszewska, Ewa Wanda; Karmanska, Anna; Aydın, Mehmet Nafiz; Aydın, Şahin
Data analysis and sharing are becoming increasingly important in creating value within food supply chains, including honey value chains. While some data is readily shared between supply chain actors, unlocking further benefits requires additional investments in digital data capturing, particularly for value-based claims such as sustainability, equity, and traceability from hives to customers. This study aims to identify critical digital data necessary for smart honey value chains to ensure traceability and transparency while fostering trust among beekeepers, intermediaries, and consumers. Semi-structured interviews with 30 beekeeping experts were conducted to explore their perspectives. The analysis identified four critical categories of data—beekeeper data, apiary data, honey data, and apiary practices data—encompassing 21 specific data points essential for ensuring transparency, traceability, and trust. These findings provide novel insights into the digital data requirements necessary to support the honey industry’s evolving needs for transparent and traceable value chains.
DroughtStats: a comprehensive software for drought monitoring and analysis
(Springer Science and Business Media Deutschland GmbH, 2025-01) Terzi, Tolga Barış; Önöz, Bihrat
The significance of drought monitoring and prediction systems has grown substantially due to the escalating impacts of climate change. However, existing tools for drought analysis face several limitations, including restricted functionality to single-variable indices, reliance on predefined probability distributions, lack of flexibility in choosing distributions, and the need for advanced programming expertise. These constraints hinder comprehensive and accurate drought assessments. This study introduces DroughtStats, a novel, user-friendly software designed to overcome these challenges and enhance drought analysis capabilities. DroughtStats integrates advanced statistical tools to analyze hydrometeorological data, compute both single-variable and multivariable drought indices using empirical and parametric methods, and evaluate drought characteristics with improved accuracy. Notably, it supports a broader range of probability distributions, performs copula-based analyses, and estimates potential evapotranspiration using multiple methods, including Penman–Monteith. Additionally, DroughtStats can analyze the relationship between different datasets using techniques like copula-based Kendall’s tau. By addressing the limitations of existing tools, DroughtStats provides a more flexible and comprehensive approach to drought monitoring. Its versatility and global applicability are demonstrated through a case study in Turkey’s Çoruh River Basin (CRB), where drought indices based on precipitation and streamflow are calculated to characterize drought conditions. The results show that DroughtStats can successfully identify and characterize drought events at various time scales, providing valuable insights into drought severity, frequency, and recovery, and offering a reliable tool for ongoing drought monitoring and management.
Advanced drought analysis using a novel copula-based multivariate index: a case study of the Ceyhan River Basin
(Springer Science and Business Media Deutschland GmbH, 2025-02) Terzi, Tolga Barış; Önöz, Bihrat
Drought is a severe natural disaster that poses significant risks to both social and ecological systems. Detecting drought is challenging due to its gradual development, which makes it difficult to identify and predict, often resulting in significant impacts on the affected regions. Therefore, accurate and dependable monitoring of drought conditions is essential for the development and implementation of effective mitigation strategies. Drought indices play a crucial role in monitoring drought conditions, with single-variable indices commonly employed in the literature to evaluate drought severity. While these indices are typically effective at characterizing the specific type of drought for which they were designed, they often fall short in offering a comprehensive view of overall drought conditions. The multivariate standardized drought index (MSDI) is a comprehensive tool that assesses drought conditions by integrating multiple hydrometeorological variables. Widely employed in the literature in both parametric and empirical forms, the MSDI is recognized for its effectiveness in detecting drought in an integrated manner. This study focuses on a particular challenge related to the calculation of MSDI using copula families. The novel methodology introduced in this paper involves selecting the most suitable copula family for each data subset using AIC and BIC criteria. Rather than applying a single copula family to the entire dataset, this approach utilizes multiple copula families for different subsets, thereby ensuring optimal modeling for each distinct group of data. The Ceyhan River Basin (CRB) is used as a case study to apply the proposed methodology. The drought characteristics of the basin are analyzed using both the newly developed MSDI and conventional single-variable indices, and the performance of the new methodology is evaluated. The application of this approach in the CRB demonstrated its effectiveness in identifying both concurrent and isolated occurrences of meteorological and hydrological droughts, thereby facilitating a more integrated and precise assessment of drought characteristics. Results indicated that the proposed MSDI detected drought events that were overlooked by single-variable indices and improved classification accuracy over the conventional MSDI.

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