Z-Skor ve Kutu Grafiğiyle Sapan Değer Belirleme

Year 2025, Volume: 12 Issue: 1, 137 - 159, 30.05.2025

Ahu Dede , Necati Ağıralioğlu

https://doi.org/10.35193/bseufbd.1471444

Abstract

Türkiye'deki 1926'dan 2012'ye kadar olan su kaynakları verileri hataya açıktır ve bu durum sıkı kalite kontrol prosedürlerini gerektirmektedir. 129 istasyondaki aylık ortalama sıcaklık, ortalama bağıl nem ve toplam yağış verilerindeki sapan değerler, z-skoru ve kutu grafikleri kullanılarak analiz edildi. Analiz sonuçları, mevsime özgü ön işleme prosedürlerinin uygulanmasının gerekliliğini ve sapan değer tespit sınırları arasındaki eşitsizliklerin belirgin olduğunu ortaya koymaktadır. Ayrıca, çeyrekler arası aralık için katsayı olarak dört değerinin kullanılmasının zorunluluğu vurgulanmıştır. Sapan değerlerinin, eksik verilere ve sapan olmayan değerlere, özellikle yaz aylarında sıfır olmayan yağış kayıtlarına yakın olma eğilimi göstermesi dikkat çekicidir. Sıcaklık ve yağış sapan değerlerinin mekansal ve zamansal açıdan benzerlikler gösterdiği belirlenmiştir. Bağıl nem ve yağış sapan değerlerini çıkaran komşu bölgeler arasında benzerlik gözlemlenmiştir. Çalışmada ayrıca, sapma miktarı ile sapan değer adedi arasında ters orantılı bir ilişki saptanmış olup, sapan değer içeren istasyonların bölgesel konumları ile bu istasyonlardaki verilerin dağılımları arasında anlamlı bir ilişki olduğu sonucuna varılmıştır.

Keywords

Hidroloji , Sapan Değer , Z-Skor , Kutu Grafiği.

Outlier Detection by Z-Score and Box Plot

Abstract

The water resources data from Turkey, spanning the period from 1926 to 2012, are susceptible to errors, necessitating rigorous quality control procedures. Outliers in the monthly average temperature, relative humidity, and total precipitation data from 129 stations were analysed using z-scores and box plots. The analysis revealed the necessity of applying season-specific preprocessing procedures and highlighted significant inequalities in the thresholds used for outlier detection. Additionally, it was emphasized that a coefficient of four should be used for the interquartile range. Notably, outliers tended to be close to missing data and non-outlier values, particularly to non-zero precipitation records during the summer months. Spatial and temporal similarities were observed in the temperature and precipitation outliers, while relative humidity and precipitation outliers exhibited similarities among neighbouring regions that generated outliers. The study also identified an inverse relationship between the magnitude of deviation and the frequency of outliers, and it found a significant correlation between the regional locations of stations containing outliers and the distribution of data within these stations.

Keywords

Hydrology , Outliers , Z-Score , Boxplot.

References

• WMO. (1993). Guide on the global data-processing system.Geneva –Switzerland WMO, 305.
• WMO. (2011).Guide to Climatological Practices. 3rd Edition, World Meteorological Organization, Geneva.
• Bayazıt, M. (1999). Hidroloji.İTU İnşaat Fakültesi Matbaası,İstanbul.
• SimavM., TürkezerA., SezenE., AkyolS., İnamM., CingözA., KılıçoğluA.(2011). Data quality control & management system of Turkish sea level monitoring network.Harita Dergisi, 145,15 -28.
• RissanenP., JacobssonC., MadsenH., MoeM., Pálsdóttir Þ.&VejenF. (2004). Nordic methods for quality control of climate data. Norwegian Meteorological Institude.
• Gürünlü AlmaÖ. (2009). Genetic algorithm based outlier detection using information criterion. Phd thesis, DEU The Graduate School of Natural and Applied Sciences, İzmir.
• BannayanM., HoogenboomG. (2008). Weatheranalogue: A toolforreal-time prediction of dailyweather data realizationsbased on a modified k- nearestneighborapproach.Environmental Modelling & Software. 23 (6), 703-713.
• Government of thePeople’sRepublic of BangladeshMinistry of WaterResources. (2015).Time Series Data Quality Control Guideline (For Hydro-meteorological data).WaterResources Planning Organization.
• BarberC., LamontagneJ.R.,VogelR. M. (2020). Improvedestimators of correlationand R2 forskewedhydrologic data.Hydrological Sciences Journal. 65 (1), 87-101.
• DikbaşF. (2018). A New Two-DimensionalRankCorrelationCoefficient.Water Resource Manage. 32, 1539–1553.
• WMO. (2016).Guidelines on Best Practices for Climate Data Rescue. World Meteorological Organization. Geneva.1182.
• MillerP. A., StanleyG. B. (1992). A system for the hourly assimilation of surface observations in mountainous and flat terrain. Monthly Weather Review. 120,2342–2359.
• National Climatic Data Center. (1993).Report of the international workshop on quality control of monthly climate data.The international workshop on quality control of monthly climate data. Asheville, NC (United States).
• WestcottN., AndsagerK., StoeckerL., SpinarM., SmithR., ObrechtR. & O’ConnellD. (2011). Quality control of 19th century weather data. Midwestern Regional Climate Center Publication.
• ToretiA.,KuglitschF. G., XoplakiE., LuterbacherJ. (2012). A Novel Approach for the Detection of Inhomogeneities Affecting Climate Time Series.Journal of Applied Meteorology and Climatology. 51 (2), 317–326.
• National Oceanic and Atmospheric Administration. (2009).Handbook of automated data quality control checks and procedures. Stennis Space Center, Mississippi: National Data Buoy Center.
• AguilarE., AuerI., BrunetM., PetersonT. C., WieringaJ. (2003). Guidelines on climate metadata and homogenization. Geneva: WMO.
• FıratM., DikbasF., KocA. C., GungorM. (2012). Analysis of temperature series: estimation of missing data and homogeneity test.Meteorological Applications. 19 (4), 1469-8080.
• BaconF. (1902). Novum Organum, 1620, ed. by J. Devey, New York: P. F. Collier.
• Bernoulli D.(1961). The Most Probable Choice Between Several Discrepant Observations and the Formation. There from of the Most Likely Induction. 1777, in C. G. Allen,Biometrika. 48, 3 – 13.
• BeckmanR. J., CookR. D. (1983).“Outlier..........s”, Technometrics, 25(2),119–149.
• HawkinsD. M. (1980). Identification of Outliers. Springer Science Business Media. B.V, Chapman & Hall.
• SinghV. P. (2017).Handbook of Applied Hydrology. Second Edition, McGraw-Hill Education: USA.
• MaityR. (2018).Statistical Methods In Hydrology And Hydroclimatology.Springer Nature, Singapore.
• LaioF., AllamanoP., ClapsP. (2010). Exploiting the information content of hydrological ‘outliers’ for goodness-of-fit testing. Hydrol. Earth Syst. Sci. 14 (10), 1909–1917.
• HershfieldD. M. (1973). “On the probability of extreme rainfall events”, Bulletin of the American Meteorological Society. 54 (10), 1013-1018.
• Van den BrinkH. W., KönnenG. P. (2008). The statistical distribution of meteorological outliers. Geophysical Research Letters. 35,23702.
• BarnettV., LewisT. (1978).Outliers in Statistical Data. Great Britain: John Wiley & Sons Ltd.
• lglewiczB., HoaglinD. C. (1993). Volume 16: How to Detect and Handle Outliers. USA: American Society for Quality Control Statistics Division.
• RousseeuwP. J., Leroy A. M. (1987). Robust Regression And Outlier Detection. USA: John Wiley & Sons.
• OvlaH. D., TaşdelenB.(2012). Aykırı Deger Yönetimi.Mersin Univ Saghk Bilim Derg. 5 (3), 1-8.
• GrubbsF. E. (1969). Procedures for Detecting Outlying Observations in Samples.Technometrics. 11(1), 1-21.
• PrescottP. (1978). Examination of the Behaviour of Tests for Outliers when More than One Outlier is Present. Journal of the Royal Statistical Society. Applied Statistics. 27(1), 10-25.
• ChandolaV., BanerjeeA., KumarV. (2009).Anomaly Detection : A Survey. ACM Computing Surveys.
• WilksD.S. (2006).Statistical Methods in the Atmospheric Sciences. Second Edition, Elsevier Inc, USA.
• Abbott P. F. & World Meteorological Organization Commission for Climatology. (1986). Guidelines on the quality control of surface climatological data. World Meteorological Organization WMO/TD. 85 (111).
• AggarwalC. C., SatheS. (2017). Outlier Ensembles, Switzerland:Springer.
• AggarwalC. C. (2017). Outlier Analysis, Switzerland:Springer.
• WangX., WangX., WilkesM.V. (2021). New Developments in Unsupervised Outlier Detection. Singapore: Springer, Xi’an Jiaotong University Press.
• SwerskyL. (2018). A Study of Unsupervised Outlier Detection for One-Class Classification. Master thesis, Department of Computing Science, University of Alberta.
• TaxD. M.J., DuinR. P.W. (2004). Support Vector Data Description. Machine Learning. 54, 45–66.
• RuffL., VandermeulenR. A., GornitzN., DeeckeL., SiddiquiS. A., BinderA., MullerE., KloftM. (2018). Deep One-Class Classification.International Conference on Machine Learning, Stockholm, Sweden.
• DawsonR. (2011). How Significant is a Boxplot Outlier?.Journal of Statistics Education. 19(2), 1-14.
• RutsI., RousseeuwP. J. (1996). Computing depth contours of Bivariate point clouds.Computational Statistics & Data Analysis. 23,153-168.
• Cárdenas-MontesM.(2014).Depth-Based Outlier Detection Algorithm. M. Polycarpou et al. (Eds.): HAIS 2014, LNAI 8480, 122–132.
• ChenY., DangX., PengH., BartH. L. (2009). Outlier Detection with the Kernelized Spatial Depth Function.Jr. IEEE transactions on pattern analysis and machine intelligence. 31(2).
• HussainI. (2020). Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology. International Journal of Advanced Computer Science and Applications. 11(9), 456-462.
• IlangoV., SubramanianR., VasudevanV. (2012). A Five Step Procedure for Outlier Analysis in Data Mining.European Journal of Scientific Research. 75(3), 327-339.
• MozharovskyiP. (2022). Anomaly detection using data depth: Multivariate case. arXiv:2210.02851v1 [stat.ML].
• Rutst I.& RousseeuwP.J. (1996). ISODEPTH: A Program for Depth Contours.Proceedings in computational statistics: 12th symposium, Barcelona, Spain.
• Ranga SuriN. N. R., NarasimhaM. M., AthithanG. (2019).Outlier Detection: Techniques and Applications. Switzerland: Intelligent Systems Reference Library, Springer. 155.
• GuptaM., GaoJ., AggarwalC., HanJ. (2014).Outlier Detection for Temporal Data. Morgan & Claypool Publishers.
• KıralG., BillorN., TürkmenA. (2013). Robust kümeleme yöntemi ile grup sapan değerlerin belirlenmesi.Ç.Ü. Sosyal Bilimler Enstitüsü Dergisi. 22(1), 189-212.
• MisraS., LiH., HeJ. (2020).Machine Learning for Subsurface Characterization. Gulf Professional Publishing.
• TranK. P. (2022).Control Charts and Machine Learning for Anomaly Detection in Manufacturing.Springer Series in Reliability Engineering, Switzerland.
• LiuF. T., Ting K. M. & ZhouZ. H. (2008).Isolation Forest.Eighth IEEE International Conference on Data Mining, Pisa, Italy. 413-422.
• PegramG. (1997). Patching rainfall data using regression methods. 3. Patching and outlier detection.Journal of Hydrology,198, 319–334.
• TürkanS., ÇetinM. C., ToktamışO. (2012). Outlier detectıon by regression diagnostics based on robust parameter estimates.Hacettepe Journal of Mathematics and Statistics. 41(1),147 – 155.
• DilmaçH., ŞişmanY. (2023). New approaches for outlier detection: The least trimmed squares adjustment. International Journal of Engineering and Geosciences. 8(1), 26-31.
• AllaS. , AdariS. K. (2019).Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch. Apress; 1st ed.
• AcarR. (2003). Çoruh hidrolojik havzasında yıllık yağış verilerinin istatistiksel modellemesi.Pamukkale Univety Engineering College Journal of Engineering Science. 9(3), 313-317.
• YeşilırmakE., AkçayS., DağdelenN., GürbüzT., SezginF. (2009). Quality control and homogeneity of annual precipitation data in Büyük Menderes Basin, Western Turkey.Fresenius Environmental Bulletin. 18, 1748-1757.
• HuntB. (2007).Climatic outliers.International Journal of Climatology. 27, 139-156.
• RadziA.B.A. (2009).Trend analysis of sea level rise for west coast of peninsular Malaysia. Master Theises, Civil – Hydrology and Water Resources, Universiti Teknologi Malaysia.
• JeongJ., ParkE., HanW. S., KimK.Y., ChoungS., ChungIl-M. (2017). Identifying Outliers of Non-Gaussian Groundwater State Data Based on Ensemble Estimation for Long-Term Trends. Journal of Hydrology. 548, 135-144.
• Ekeu-weiI.T., BlackburnG.A., PedrucoP. (2018). Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions.Water. 10, 1483.
• KöseÖ. (2010). Characteristics of Various Filters Used to Eliminate Outliers in Velocity Time Series Obtained Using ADVs.VI. Ulusal Hidroloji Kongresi,Pamukkale Üniversitesi, Denizli. 360-368.
• NooneS., MurphyC., CollJ., MatthewsT., MullanD., WilbyR., WalshS. (2015). Homogenization and analysis of an expanded long-term monthly rainfall network for the Island of Ireland (1850–2010).International Journal of Climatology. 36, 2837-2853.
• GottschalkL., KundzewiczZ. (1995). Analysis of outliers in Norwegian flood data. New Uncertainty Concepts in Hydrology and Water Resources, Cambridge University Press. 245-251.
• KirkA., McCuenR. (2008). Outlier Detection in Multivariate Hydrologic Data.Journal of Hydrologic Engineering.13(7), 641-646.
• HansenJ., RuedyR., SatoM., LoK. (2010). Global surface temperature change. The American Geophysical Union. Reviews of Geophysics. 48.
• DomonkosP. (2006). Testing of homogenization methods porpuses, tools and problems of implementation. Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, Climate Data and Monitoring WCDMP-No. 71.
• DomonkosP. (2011). Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT).International Journal of Geosciences. 2(3), 293-309.
• Vicente-SerranoS. M., Beguer´ıaS., L´opez-MorenoJ. I., Garc´ıa-VeraandPetrStepanekM. A. (2010). A complete daily precipitation database for northeast Spain: reconstruction, quality control, and homogeneity.International Journal Climatology. 30, 1146–1163.
• YakuninA., MogahedH. (2018). Improvement of statistical methods for detecting anomalies.Climate and Environmental Monitoring Systems.289.
• ShabaniS., SamadianfardS., SattariM. T., MosaviA., ShamshirbandS., KmetT., Várkonyi-KóczyA. R. (2020). Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forestand Support Vector Machines; Comparative Analysis. Atmosphere. 11(66).
• KlokEJ., KleinT. AMG. (2009). Updated and extended European dataset of daily climate observations.International Journal of Climatology. 29(8), 1182–1191.
• HofstraN., NewM., McSweeneyC. (2010). The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data.Clim Dyn. 35, 841–858.
• WuH., TangX., WangZ., WuL., LuM., LianfangW., ZhuJ. (2018). Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network.Advances in Atmospheric Sciences. 35, 1522-1532.
• ChowV., MaidmentD., Mays L. (2013).Applied Hydrology. 2nd Edition, McGraw-Hill Companies.
• OğuzE.B., ÖnözB. (2003). Assessment of outliers in statistical data analysis.Nato Science Series: IV: Earth and Environmental Sciences. 23, 173-180.
• AminM.T., RizwanM., AlazbaA.A. (2015). Comparison of mixed distribution with EV1 and GEV components for analyzing hydrologic data containing outlier. Environmental Earth Sciences. 73(3), 1369-1375.
• CiupakM., Ozga-ZielinskiB., TokarczykT., AdamowskiJ. (2021). A Probabilistic Model for Maximum Rainfall Frequency Analysis. Water. 13, 2688.
• SallabandaA. (1996). Homogeneity evaluation of snow cover records in Albania.Proceedings of the First Seminar for Homogenization of Surface Climatological Data, Budapest. 6–12.
• ShabaniS., SamadianfardS., SattariM., MosaviA., BandS., KmeťT., Varkonyi-KoczyA. (2020). Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis. Atmosphere. 11, 1-17.
• Fernández-PalominoC.A., Lavado-CasimiroW.S. (2017). Regional maximum rainfall analysis using L-moments at the Titicaca Lake drainage, Peru. Theor Appl Climatol.129,1295–1307.
• AslanS. (2008). Ege Bölgesi günlük maksimum yağışlarının bölgesel analizi. Dokuz Eylül Üniversitesi FBE, yüksek lisans tezi.
• LamontagneJ., StedingerJ. (2015). Examination of the Spencer-McCuen Outlier-Detection Test for Log-Pearson Type 3 Distributed Data. Journal of Hydrologic Engineering. 21,04015069.
• CohnT., EnglandJ., BerenbrockC., MasonR., StedingerJ., LamontagneJ. (2013). A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series. Water Resources Research. 49, 5047-5058.
• KaynarL. (2005). Doğu Karadeniz bölgesi yıllık maksimum akımlarının istatistik methodlarla analizi. İTU, FBE, yüksek lisans tezi.
• FaybishenkoB., VersteegR., PastorelloG. (2022). Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data. Stoch Environ Res Risk Assess. 36, 1049–1062.
• AltayL. (2011).Konum-zaman bağlamında aykırı gözlem tespit yöntemlerinin incelenmesi ve bulanık Kümelendirme tabanlı bir metot.Yüksek lisans tezi. Hava harp okulu, havacılık ve uzay teknolojileri enstitüsü.
• LanzanteJ.R. (1996). Resistant, Robust and Non-Parametric Techniques for the Analysis of Climate Data: Theory and Examples, Including Applications to Historical Radiosonde Station Data.International Journal of Climatology.16, 1197-1226.
• SheeseP., BooneC., WalkerK. (2015). Detecting physically unrealistic outliers in ACE-FTS atmospheric measurements.Atmospheric Measurement Techniques. 8,741-750.
• LiJ.S., HamannA., BeaubienE. (2020). Outlier detection methods to improve the quality of citizen science data. Int J Biometeorol. 64(11), 1825-1833.
• EstévezJ., GavilánP., García-MarínA. (2018). Spatial regression test for ensuring temperature data quality in southern Spain.Theoretical and Applied Climatology. 131, 309-318.
• TellezB., CernockyT., & TerradellasE. (2008). Calculation of climatic reference values and its use for automatic outlier detection in meteorological datasets.Advances in Science and Research. 2, 1-4.
• LenwoodW. H. JR., KillenW. D., AndersonR. D., PerryE. (2004). Analysis of historical salinity and boron surface water monitoring data from the San Joaquin River watershed: 1985-2002.Environmental monitoring and assessment. 95(1-3), 125–151.
• ErdoğanG. (2012).Spectral methods for outlier detection in machine learning. The Institute for Graduate Studies in Science and Engineering. Master of Science, Boğaziçi University.
• LongmanR. J., NewmanA. J., GiambellucaT. W., LucasM. (2020). Characterizing the Uncertainty and Assessing the Value of Gap-Filled Daily Rainfall Data in Hawaii.American Meteorological Society.1261 – 1276.
• ZhaoC., YangJ. (2019). A Robust Skewed Boxplot for Detecting Outliers in Rainfall Observations in Real-Time Flood Forecasting. Advances in Meteorology.1-7.
• BhattacharyaS., BeirlantJ. (2019). Outlier detection and a tail-adjusted boxplot based on extreme value theory.
• SharifM., BurnD. H. (2006). Simulating climate change scenarios using an improved K-nearest neighbor model.Journal of Hydrology.325, 179–196.
• BoulangerJP., AizpuruJ., LeggieriL., Et al (2010). A procedure for automated quality control and homogenization of historical daily temperature and precipitation data. (APACH): part 1: Quality control and application to the Argentine weather service stations, Climatic Change. 98, 471–491.
• FreitasL., PereiraM., CarameloL., MendesM., NunesL. (2013). Homogeneity of monthly air temperature in Portugal with HOMER and MASH”, Idojaras. 117, 69-90.
• Murav’evA.V., KulikovaI.A., KruglovaE.N. (2009). Distribution of extreme characteristics of atmospheric circulation from reanalysis data and hydrodynamic modeling. Russ. Meteorol. Hydrol.34, 431–441.
• NaghettiniM. (2017).Fundamentals of Statistical Hydrology. Springer, Switzerland.
• FarmerW. H., VogelR. M. (2013). Performance-weighted methods for estimating monthly stream flow at ungauged sites.Journal of Hydrology. 477, 240–250.
• LiuD., LiuH., MengX. (2023). Advanced Hydrologic Modeling in Watershed Scales. MDPI – Multidisciplinary Digital Publishing Institute.
• GonzálezR., Fidel J.& JiménezJ., QuesadaV., ValeroF. (2001). Quality Control and Homogeneity of Precipitation Data in the Southwest of Europe.Journal of Climate. 14, 964-978.
• MeenaH. M., MachiwalD., SantraP., MoharanaP., SinghDr. (2019). Trends and homogeneity of monthly, seasonal, and annual rainfall over arid region of Rajasthan, India. Theoretical and Applied Climatology. 136, 795-811.
• GöktürkO.M., BozkurtD., SenO., KaracaM. (2008). Quality control and homogeneity of Turkish precipitation data. Hydrological Processes. 22, 3210 – 3218.
• ZaifogluH., AkintugB., YanmazA. (2018). Quality and Homogeneity Analysis of Precipitation Data in North Cyprus. Conference: 13th International Congress on Advances in Civil Engineering, İzmir, Turkey.
• ŠtěpánekP., ZahradníčekP. (2008). Experiences with quality control and homogenization of daily series of various meteorological elements in the Czech Republic 1961-2007.Proceedings of the sixth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, Climate Data and Monitoring WCDMP-No. 76.
• DunnR. J. H., WillettK. M., ThorneP. W., WoolleyE. V., DurreI., DaiA., ParkerD. E., Vose R. S. (2012).HadISD: a quality-controlled global synoptic report database for selected variables at long-term stations from 1973–2011.Clim. Past, 8, 1649–1679.
• ToretiA., DesiatoF. (2006). Homogenization and validity controls for temperature trend estimates over Italy. Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, Climate Data and Monitoring WCDMP-No. 71.
• ŠtěpánekP., ŘezníčkováL., BrázdiR. (2006). Homogenization of daily air pressure and temperature series for Brno (Czech Republic) in the period 1848-2005.Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, Climate Data and Monitoring WCDMP-No. 71.
• Štěpánek P., MikulováK. (2006). Homogenization of air temperature and relative humidity monthly means of individual observation hours in the area of The Czech and Slovak Republic. Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, Climate Data and Monitoring WCDMP-No. 71.
• ShiJ., YangL. (2020). A Climate Classification of China through k-Nearest-Neighbor and Sparse Subspace Representation.Journal of Climate. 33(1), 243-262.
• BorgesP., FrankeJ., AnunciaçãoY., WeissH., BernhoferC. (2015). Comparison of spatial interpolation methods for the estimation of precipitation distribution in Distrito Federal, Brazil.Theoretical and Applied Climatology.
• EstévezJ., GavilánP., García-MarínA.P. (2011). Data validation procedures in agricultural meteorology – a prerequisite for their use. Adv. Sci. Res.6, 141–146.
• AydınM., TunaA. (2018). Aykırı Değerlerin Tespiti ve Taşkın Debilerinin Hesabı için Bir Yöntem Geliştirilmesi. Teknik Dergi. 8339-8363.
• AydınM. (2015). Hidrolojik Verilerdeki Aykırı Değerlerin Taşkın Frekans Analizi Üzerine Etkisinin İncelenmesi: Van Gölü Havzası Örneği.Journal of The Institute of Natural & Applied Sciences. 20(1-2), 47-55.
• El KenawyA., L´opez-MorenoJ. I., StepanekcandP., Vicente-SerranoaS. M. (2013). An assessment of the role of homogenization protocol in the performance of daily temperature series and trends: application to north eastern Spain. International Journal Climatology. 33,87–108.
• KuhnN. J., BaumhauerR., SchüttB. (2011). Managing the impact of climate change on the hydrology of the Gallocanta Basin, NE-Spain. Journal of Environmental Management. 92(2), 275-283.
• McMahonT. A., PeelM. C. (2019). Uncertainty in stage–discharge rating curves: application to Australian Hydrologic Reference Stations data.Hydrological Sciences Journal. 64(3), 255-275.
• DeGagneaM. P.J., DouglasG. G., HudsonH. R., SimonovicS. P. (1996). A decision support system for the analysis and use of stage-discharge rating curves.Journal of Hydrology.184, 225-241.
• AydınM. (2012). Hidrolojik verilerdeki aykırı değerlerin tahmini için bir method geliştirilmesi ve Dicle ile Fırat havzalarına uygulanması. Yüksek Lisans Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü.
• ChebanaF., Dabo-NiangS., OuardaT. (2012). Exploratory functional flood frequency analysis and outlier detection. Water Resources Research. 48, 4514.
• SuhailaJ. (2021). Functional Data Visualization and Outlier Detection on the Anomaly of El Niño Southern Oscillation. Climate. 9(118).
• ChebanaF., OuardaaT. (2011). Multivariate extreme value identification using Depth functions.Environmetrics. 2, 441–455.
• RomanoE., MateuJ. (2013). Outlier Detection for Geostatistical Functional Data: An Application to Sensor Data. A.Giusti et al. (eds.), Classification and Data Mining, Studies in Classification, Data Analysis and Knowledge Organization, Springer-Verlag, Berlin Heidelberg. 131-138.
• ChebanaF., OuardaT. (2011). Depth-based multivariate descriptive statistics with hydrological Applications. Journal of geophisical research. 116, 1-19.
• WaznehH. (2016). Identification of hydrological neighborhoods for regional flood frequency analysis using statistical depth function.Advances in Water Resources.
• EichingerF., et al. (2013). Data Analysis Challenges in the Future Energy Domain. International Symposium on Intelligent Data Analysis.
• JenniferL., AndreasH., ElisabethB. (2020). Outlier detection methods to improve the quality of citizen science data. International Journal of Biometeorology. 64, 1825-1833.
• LuY., KumarJ., CollierN., Krishna B.& LangstonM. A. (2018). Detecting Outliers in Streaming Time Series Data from ARM Distributed Sensors.IEEE International Conference on Data Mining Workshops (ICDMW), Singapore. 779-786.
• PetkovskiA., ShehuV. (2023). Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using K-Means, Isolation Forest, and Local Outlier Factor.12th Mediterranean Conference on Embedded Computing.
• YuQ., YuanShengL. (2019). Hydrological Time Series Anomaly Pattern Detection based on Isolation Forest.IEEE 3rd Information Technology,Networking,Electronic and Automation Control Conference.
• AmirpouyaS. (2015). Flood Outlier Detection Using PCA and Effect of How to Deal with Them in Regional Flood Frequency Analysis via L-Moment Method. Water Resources. 42, 448-459.
• AkyayT., KarslıoğluM. O. (2009). Wavelet-Based Outlier Detection of Airborne Laser Scanning Data. 4th International Conference on Recent Advances in Space Technologies, İstanbul, Turkey. 426-427.
• IhC., GeorgeT., ChungC. (1988). Estimation of Time Series Parameters in the Presence of Outliers. Technometrics. 30, 193-204.
• MarinovaT.,AlexandrovV. (2006). On homogenization of climate long-term series in Bulgaria. Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Climate Data and Monitoring WCDMP-No. 71.
• PanagiotaG., PanayotisP. (2007). Outliers and trend detection tests in rainfall extremes. Proceedings of the 32nd IAHR World Congress, Venice.
• AkarA.U. (2021).GNSS gözlemlerindeki troposferik gecikmenin destek vektör makineleri algoritması ile kestirimi. Yüksek Lisans Tezi, Harita Mühendisliği Anabilim Dalı, Konya Teknik Üniversitesi.
• GiustariniL., ParisotO., GhoniemM., HostacheR., TrebsI., OtjacquesB. (2016). A user-drivencase-based reasoning tool for infilling missing values in daily mean river flow records.Environmental Modelling & Software. 82, 308-320.
• Wakdikar M. P. & Deo M. C. (1999). Outlier detection and effects in dynamic modeling.ISH Journal of Hydraulic Engineering. 5(1), 33-41.
• DedeA. (2023).Hidrolojik sapan değer tespitine yeni bir yaklaşım.Doktora Tezi, İTU.
• DedeA., AğıralioğluN. (2023). Hidrolojik Sapan Değer Tespitinde Komşu İstasyon Seçimi.Fırat Üniversitesi Mühendislik Bililmleri Dergisi.35(1), 81-99.
• González-HidalgoJ. C., ArrillagaM. de L., StepanekP., LanjeriS. (2006). Quality control of monthly precipitation data from Mediterranean areas of Spain. Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary,Climate Data and Monitoring WCDMP-No. 71.
• LundR., WangX. L., ReevesJ., FengY., LuQ., GallagherC. (2006). Changepoint Detection in Periodic and Autocorrelated Time Series.Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, Climate Data and Monitoring WCDMP-No. 71.
• GuijarroJ. A. (2006). Homogenization of a dense thermo-pluviometric monthly database in the Balearic Islands using the free contributed R package ‘Climatol’.Proceedings of the Fifth Seminar for Homogenization and Quality Control in Climatological Databases, Budapest, Hungary, Climate Data and Monitoring WCDMP-No. 71.
• YangJ., ChenY., RahardjaS. (2022). Neighborhood Representative for Improving Outlier Detectors.Information Sciences. 625.
• WangX. L., FengY. (2010).RHtestsV3 User Manual. Science and Technology Branch, Environment Canada, Climate Research Division, Atmospheric Science and Technology Directorate. Toronto: Canadian Centre for Climate Modelling and Analysis.
• Water Resources Planning Organization. (2015). Time Series Data Quality Control Guideline (For Hydro-meteorological data). Government of the People’s Republic of Bangladesh Ministry of Water Resources.
• SevrukB., GeigerH. (1981).Selection of distribution types for extremes of precipitation. World Meteorological Organization (WMO), Operational hydrology report (OHR) No. 15.
• HarveyJ. E. R., MaxA. L. (2015).Understanding Mathematical And Statistical Techniques in Hydrology An Examples-Based Approach. John Wiley & Sons, UK.
• YalçınG. D. (2005). Klimatoloji – I. Ankara: DMİ Genel Müdürlüğü Matbaası.