Earth's Future (EF) Geophysical Research Letters (GRL) Water Resources Research (WRR) Hydrology and Earth System Sciences (HESS) Hydrological Sciences Journal (HSJ) Hydrology Research (HR) Stochastic Environmental Research and Risk Assessment (SERR) Applied Soft Computing (ASC)
Machine learning and hydrological sciences: a systematic overview of review papers [lead author, community review paper by the EURO-FRIEND (UNESCO-IHP FRIEND-Water Programme) project 3 “Large Scale Variations in Hydrological Characteristics”] Machine learning and hydrological sciences: a community survey [lead author, community review paper by the EURO-FRIEND (UNESCO-IHP FRIEND-Water Programme) project 3 “Large Scale Variations in Hydrological Characteristics”]
Deep Huber quantile regression networks [co-author, w/ Hristos Tyralis (lead), Georgia Papacharalampous, Kwok Chun] - in review, Neural Networks. Preprint available at: https://arxiv.org/abs/2306.10306 Leveraging big data and AI for disaster risk management: interdisciplinary legal approaches [co-author, w/ Thanti Octavianti et al.] - in review, WIREs Water.
Clustering in hydrology: a systematic review and research outlook [lead author, w/ Svenja Fischer, Wouter Knoben, Manuela I. Brunner] Comparison of clustering methods [co-author, w/ Georgia Papacharalampous (lead), Svenja Fischer, Hristos Tyralis]
*** published:
Dogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. (2015)Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments, Hydrology and Earth System Sciences, 19, 3181-3201, doi:10.5194/hess-19-3181-2015.
Dogulu, N.(2013)Predictive uncertainty of flood models: Experiments with the UNEEC method on several contrasting case studies. MSc Thesis: WSE-FRM.13-06, UNESCO-IHE Institute for Water Education, Delft, the Netherlands.
Dogulu, N., A. O. Olusola, and G. Papacharalampous. (2023)Machine learning and hydrological sciences: A systematic overview of review papers. Abstract EGU23-10575, EGU General Assembly 2023, Vienna, Austria, 23 – 28 April 2023.LINK
Dogulu, N., M. I. Brunner, S. Fischer, and W. Knoben. (2022)Clustering for hydroclimatic extremes: a retrospective synthesis of the literature. Abstract IAHS2022-262, IAHS 2022 Scientific Assembly, Montpellier, France, 29 May – 3 June 2022. https://doi.org/10.5194/iahs2022-262 LINK(Oral presentation)PPT Dogulu, N.(2022)Applications of Clustering Methods in Hydrology. The University of Texas at Austin, Jackson School of Geosciences – Water, Climate and Environment Seminar Series (Spring 2022), Online. 06 May 2022. LINK (INVITED Talk)
Dogulu, N.(2019)Understanding hydrology and cosmos: the role of catchments. Abstract #287881, 10th EGU Leonardo Conference on Earth’s Hydrological Cycle: Global change, landscape ageing and the pulse of catchments, Esch-sur-Alzette, Luxembourg, 16 – 18 October 2019. LINK (Poster presentation)
Dogulu, N.(2019)Clustering Algorithms: Perspectives from the Hydrology Literature. Abstract IUGG19-3031, 27th IUGG General Assembly, IAHS Symposia, Montréal, Canada, 9 – 14 July 2019. LINK (Poster presentation)
Dogulu, N. (2019)Exploratory insights into hydrological applications of data clustering. Geophysical Research Abstracts, Vol. 21, EGU2019-12099, European Geosciences Union General Assembly, Vienna, Austria, 7 – 12 April 2019. LINK (Oral presentation)
Dogulu, N., I. Batmaz and E. Kentel. (2018)Clustering approaches for analysing similarity in ungauged catchments: input variable selection for hydrological predictions. Geophysical Research Abstracts, Vol. 20, EGU2018-13146, European Geosciences Union General Assembly, Vienna, Austria, 8 – 13 April 2018. LINK(Poster presentation)
Dogulu, N., I. Batmaz and E. Kentel. (2018)Input variable selection for hydrological predictions in ungauged catchments: with or without clustering? Geophysical Research Abstracts, Vol. 20, EGU2018-14438-1, European Geosciences Union General Assembly, Vienna, Austria, 8 – 13 April 2018. LINK (Poster presentation)
Dogulu, N., and E. Kentel. (2017)Clustering of hydrological data: a review of methods for runoff predictions in ungauged basins. Geophysical Research Abstracts, Vol. 19, EGU2017-12005, European Geosciences Union General Assembly, Vienna, Austria, 23 – 28 April 2017. LINK (Poster presentation)
Dogulu, N., and E. Kentel. (2016)Exploring the added value of machine learning methods in predicting flow duration curves: a comparative analysis for ungauged catchments. AGU abstract H31F-1452, 2016 American Geophysical Union Fall Meeting, San Francisco, USA, 12 – 16 December 2016. LINK (Poster presentation)
Dogulu, N., and E. Kentel. (2015)Estimation of flow duration curve for ungauged catchments using adaptive neuro-fuzzy inference system and map correlation method: a case study from Turkey. AGU abstract H33A-1559, 2015 American Geophysical Union Fall Meeting, San Francisco, USA, 14 – 18 December 2015. LINK (Poster presentation)
Dogulu, N., D. P. Solomatine, and D. L. Shrestha. (2014)Applying clustering approach in predictive uncertainty estimation: a case study with the UNEEC method. Geophysical Research Abstracts, Vol. 16, EGU2014-5992, European Geosciences Union General Assembly, Vienna, Austria, 27 April – 2 May 2014. LINK (Poster presentation)
IAHS Symposia (2019) at the 27th IUGG General Assembly / Co-convener: H20 – Predictions in ungauged basins: what’s new? [w/ Thomas Skaugen, Gil Mahe, Aldo Fiori, Michelle Newcomer, Honeyeh Iravani]