Detecting seasonal cycle shift on streamflow over Turkey by using multivariate statistical methods

Yildiz, Doğan; Güneş, Mehmet Şamil; Yavuz, Fulya Gokalp; Yildiz, Dursun

doi:10.1007/s00704-017-2242-2

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Using the software "gCLUTO" version 1.0 (http://glaros.dtc. umn.edu/gkhome/cluto/gcluto/download), a binary matrix with important standard terms as rows and source articles as columns was created in BICOMB for further dichotomous clustering (25)(26)(27)(28). Parameters for dichotomous clustering in gCLUTO were adjusted to match those recommended for dichotomous cluster analysis.…”

Section: Bibliometric Analysismentioning

confidence: 99%

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

et al. 2022

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BackgroundArtificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC.MethodsScientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords.ResultsThe total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified.ConclusionAAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.

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Section: Bibliometric Analysismentioning

confidence: 99%

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

et al. 2022

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“…Algan (2006) emphasized the importance of the major rivers (e.g., Kocasu/Cross, Biga, and Gonen located in the southern watersheds) in transporting suspended sediment loads. By using multidimensional scaling for all the Turkish stream flows, Yildiz et al (2018) recently showed that there has been a seasonal shift in flowrates over the last four decades due to climate change. This necessitates detailed regional studies on coastal discharges in relation to climate change.…”

Section: Introductionmentioning

confidence: 99%

Climate change impacts on river discharge to the Sea of Marmara

Basdurak

2023

Front. Mar. Sci.

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The Sea of Marmara, located in Northwestern Türkiye, is under multiple stressors, including climate change and industrial, agricultural, and domestic pollution, that cause deoxygenation in coastal waters, with multiregional consequences affecting the surface and deep-water masses transported to the Mediterranean and Black Seas, respectively, via its straits. With climate-change driven changes in the intensity of extreme precipitation events, the marine environment becomes more vulnerable to increasing terrestrial pollutants. Evaluating the spatial and temporal variation of river runoff is crucial to understanding the interaction between the geophysical and hydrogeochemical processes that affects the nutrient balance of the sea. This study aims to (i) explore the historical (for the period 1960-2021) and spatial changes of monthly-averaged coastal discharges along the coastline of the Sea of Marmara for the first time, based on observations from the national hydrological service; (ii) analyze the change in long-term and seasonal trends of runoff and net-precipitation rate and derive a regional relation between the two parameters. Single Spectrum Analysis (SSA) is used to obtain the trends. Gaps in the time series are filled in using a non-parametric spectral estimation method. Discharges from the northern, eastern, and southern basins are, respectively, 3%, 17%, and 80% of the total discharge, which has varied between 1.5 and 15 km3 per year in the last decade, with short-lived extremes occurring in early spring. Total runoff rate shows a declining long-term trend that is accelerating with increasing evaporation. The intensity of the terrestrial precipitation extremes shows a temporal increase; there is a quadratic relation between the long-term trends of net precipitation and total runoff. Quantification of nutrient load distribution along the coastline associated with the spatial-temporal changes in coastal fluxes is urgent because the cumulative stressors (warming, nutrient overenrichment, pollutants) pose a threat of triggering extreme events and eutrophication in the Sea of Marmara with multiregional impact.

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Detecting seasonal cycle shift on streamflow over Turkey by using multivariate statistical methods

Cited by 2 publications

References 27 publications

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

Climate change impacts on river discharge to the Sea of Marmara

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