Výsledky vyhledávání - "Özlem Baydaroğlu"

Akademický článek

Temporal and spatial satellite data augmentation for deep learning-based rainfall nowcasting

Publikováno v: Journal of Hydroinformatics, Vol 26, Iss 3, Pp 589-607 (2024)

The significance of improving rainfall prediction methods has escalated due to climate change-induced flash floods and severe flooding. In this study, rainfall nowcasting has been studied utilizing NASA Giovanni satellite-derived precipitation produc

Externí odkaz: https://doaj.org/article/db3fe6e3e9a7439684c934682d1350f6

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Akademický článek

A contemporary systematic review of cyberinfrastructure systems and applications for flood and drought data analytics and communication

Autor: Serhan Yeşilköy, Özlem Baydaroğlu, Nikhil Singh, Yusuf Sermet, Ibrahim Demir

Publikováno v: Environmental Research Communications, Vol 6, Iss 10, p 102003 (2024)

Hydrometeorological disasters, including floods and droughts, have intensified in both frequency and severity in recent years. This trend underscores the critical role of timely monitoring, accurate forecasting, and effective warning systems in facil

Externí odkaz: https://doaj.org/article/3fa9b225d5674011818a4c024168e254

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A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Autor: Özlem Baydaroğlu Yeşilköy, Serhan Yeşilköy, Muhammed Sermet, Ibrahim Demir

Open science presents a new approach for knowledge discovery, dissemination, and integrity. The idea behind open science is to reinforce research activities and create open knowledge networks by exploring, organizing, and sharing scientific data, as

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::88a7fbfbe4f39306ee4c31f0dcc80fbd
https://doi.org/10.31223/x5ss74

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Prediction of commonly used drought indices using support vector regression powered by chaotic approach

Autor: Yeşilköy, Özlem Baydaroğlu, Koçak, Kasım, Saylan, Levent

An effective water resources management requires accurate predictions of possible risks. Drought is one of the most devastating phenomena that has a certain risk of occurrence. Understanding the variability of the drought indices is of great importan

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cfe357fe668d13698680839c909e772

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Estimation of PM10 concentrations in Turkey based on Bayesian maximum entropy

Autor: Özlem Baydaroğlu Yeşilköy

Publikováno v: Volume: 4, Issue: 1 48-55
International Advanced Researches and Engineering Journal

Spatial and temporal distribution of PM10 is modeled by Bayesian Maximum Entropy (BME) method. It is the spatiotemporal estimation method which combines exact measurements with the secondary information by considering local uncertainties. In this stu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd11513152a502c31c925591d6a8de2a
https://app.trdizin.gov.tr/makale/TXpNMk9ETTJOZz09

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River flow prediction using hybrid models of support vector regression with the wavelet transform, singular spectrum analysis and chaotic approach

Autor: Kasım Koçak, Özlem Baydaroğlu, Kemal Duran

Publikováno v: Meteorology and Atmospheric Physics. 130:349-359

Prediction of water amount that will enter the reservoirs in the following month is of vital importance especially for semi-arid countries like Turkey. Climate projections emphasize that water scarcity will be one of the serious problems in the futur

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::50c76d40d041878ca7b119384276e1ff
https://doi.org/10.1007/s00703-017-0518-9

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Spatiotemporal analysis of wind speed via the Bayesian maximum entropy approach

Autor: Özlem Baydaroğlu, Kasım Koçak

Publikováno v: Environmental Earth Sciences. 78

Kocak, Kasim/0000-0002-5611-0298; BAYDAROGLU, Ozlem/0000-0003-2184-5785 WOS:000455022900022 Spatiotemporal mapping of wind speed data is of great importance on determining the wind energy potential, construction zones, projecting future values and pl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6622913e57d3e8dd391b17d6bc2ca40f
https://doi.org/10.1007/s12665-018-8024-z

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SVR-based prediction of evaporation combined with chaotic approach

Autor: Kasım Koçak, Özlem Baydaroğlu

Publikováno v: Journal of Hydrology. 508:356-363

Summary Evaporation, temperature, wind speed, solar radiation and relative humidity time series are used to predict water losses. Prediction of evaporation amounts is performed using Support Vector Regression (SVR) originated from Support Vector Mach

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ec078f013ff709deb49979240b618294
https://doi.org/10.1016/j.jhydrol.2013.11.008

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