@article{MTMT:34522726,
	title = {Microplastic Contamination of Fine-Grained Sediments and Its Environmental Driving Factors along a Lowland River: Three-Year Monitoring of the Tisza River and Central Europe},
	url = {https://m2.mtmt.hu/api/publication/34522726},
	author = {Balla, Alexia and Teofilovic, Vesna and Kiss, Tímea},
	doi = {10.3390/hydrology11010011},
	journal-iso = {HYDROLOGY-BASEL},
	journal = {HYDROLOGY},
	volume = {11},
	unique-id = {34522726},
	abstract = {The hydro-geomorphological background in microplastic (MP) deposition and mobilization is often neglected, though the sampling environment is the key point in a monitoring scheme. The aim of the study was to analyze the environmental driving factors of MP transport over three years (2020–2022) along a 750 km-long section of the Tisza River, Central Europe. The mean MP content of the fresh clayey sediments was 1291 ± 618 items/kg in 2020, and then it decreased (2021: 730 ± 568 items/kg; 2022: 766 ± 437 items/kg). The upstream and downstream sections were the most polluted due to improper local sewage treatment. In 2020, 63% of the sites were hotspot (≥2000 items/kg), but their number decreased to one-third in 2021 and 2022. MP pollution is influenced by highly variable environmental factors. (1) The geomorphological setting of a site is important, as most of the hotspots are on side bars. (2) The tributaries convey MP pollution to the Tisza River. (3) The bankfull or higher flood waves effectively rearrange the MP pollution. (4) The dams and their operation influence the downstream trend of MP pollution in the reservoir. (5) Downstream of a dam, the clear-water erosion increases the proportion of the pristine sediments; thus, the MP concentration decreases.},
	year = {2024},
	eissn = {2306-5338},
	pages = {11},
	orcid-numbers = {Balla, Alexia/0000-0003-0829-953X; Teofilovic, Vesna/0000-0002-3557-1482; Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:34531362,
	title = {Water level prediction using long short-term memory neural network model for a lowland river: a case study on the Tisza River, Central Europe},
	url = {https://m2.mtmt.hu/api/publication/34531362},
	author = {Vizi, Zsolt and Batki, Bálint and Rátki, Luca and Szalánczi, S and Fehérváry, István and Kozák, Péter and Kiss, Tímea},
	doi = {10.1186/s12302-023-00796-3},
	journal-iso = {ENVIRON SCI EUR},
	journal = {ENVIRONMENTAL SCIENCES EUROPE},
	volume = {35},
	unique-id = {34531362},
	issn = {2190-4707},
	year = {2023},
	eissn = {2190-4715},
	orcid-numbers = {Vizi, Zsolt/0000-0003-2568-8633; Fehérváry, István/0000-0002-2519-2008; Kozák, Péter/0000-0001-9391-2823; Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:34407342,
	title = {Riverine Microplastic Quantification: A Novel Approach Integrating Satellite Images, Neural Network, and Suspended Sediment Data as a Proxy},
	url = {https://m2.mtmt.hu/api/publication/34407342},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kovács, Ferenc and Kiss, Tímea},
	doi = {10.3390/s23239505},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {23},
	unique-id = {34407342},
	abstract = {Rivers transport terrestrial microplastics (MP) to the marine system, demanding cost-effective and frequent monitoring, which is attainable through remote sensing. This study aims to develop and test microplastic concentration (MPC) models directly by satellite images and indirectly through suspended sediment concentration (SSC) as a proxy employing a neural network algorithm. These models relied upon high spatial (26 sites) and temporal (198 samples) SSC and MPC data in the Tisza River, along with optical and active sensor reflectance/backscattering. A feedforward MLP neural network was used to calibrate and validate the direct models employing k-fold cross-validation (five data folds) and the Optuna library for hyperparameter optimization. The spatiotemporal generalization capability of the developed models was assessed under various hydrological scenarios. The findings revealed that hydrology fundamentally influences the SSC and MPC. The indirect estimation method of MPC using SSC as a proxy demonstrated higher accuracy (R2 = 0.17–0.88) than the direct method (R2 = 0–0.2), due to the limitations of satellite sensors to directly estimate the very low MPCs in rivers. However, the estimation accuracy of the indirect method varied with lower accuracy (R2 = 0.17, RMSE = 12.9 item/m3 and MAE = 9.4 item/m3) during low stages and very high (R2 = 0.88, RMSE = 7.8 item/m3 and MAE = 10.8 item/m3) during floods. The worst estimates were achieved based on Sentinel-1. Although the accuracy of the MPC models is moderate, it still has practical applicability, especially during floods and employing proxy models. This study is one of the very initial attempts towards MPC quantification, thus more studies incorporating denser spatiotemporal data, additional water quality parameters, and surface roughness data are warranted to improve the estimation accuracy.},
	year = {2023},
	eissn = {1424-8220},
	orcid-numbers = {Kovács, Ferenc/0000-0001-7944-8921; Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:34107572,
	title = {Increased Riparian Vegetation Density and Its Effect on Flow Conditions},
	url = {https://m2.mtmt.hu/api/publication/34107572},
	author = {Kiss, Tímea and Fehérváry, István},
	doi = {10.3390/su151612615},
	journal-iso = {SUSTAINABILITY-BASEL},
	journal = {SUSTAINABILITY},
	volume = {15},
	unique-id = {34107572},
	abstract = {The physical and biological structure of riparian vegetation fundamentally influences floodplain roughness, and thus the flood velocity and flood levels of a river. The study aims to provide detailed spatial data on the vegetation density of a floodplain, and to model the effect of the actual vegetation and various scenarios on flow conditions. LiDAR data were applied to evaluate the density and roughness of the submerged understory vegetation over the densely vegetated floodplain of Lower Tisza, Hungary. Then, HEC–RAS 2D modelling was applied to analyse the effect of the actual vegetation on flow conditions. Further scenarios were also created to predict the effect of (i) invasive plant control, (ii) no maintenance, and (iii) riparian vegetation restoration (meadows). According to the results, since the 19th Century, the increased vegetation density is responsible for a 17-cm flood level increase, and if the vegetation grows even denser, a further 7 cm could be expected. As the vegetation density increases, the overbank flow velocity decreases, and the crevasses and flood conveyance zones gradually lose their function. Simultaneously, the flow velocity increases in the channel (from 1 m/s to 1.4 m/s), resulting in an incision. Applying LiDAR-based 2D flow modelling makes it possible to plan sustainable riparian vegetation maintenance (e.g., forestry, invasive species clearance) from both ecology and flood control perspectives.},
	year = {2023},
	eissn = {2071-1050},
	orcid-numbers = {Kiss, Tímea/0000-0002-2597-5176; Fehérváry, István/0000-0002-2519-2008}
}

@article{MTMT:34094864,
	title = {Quantification of macroplastic litter in fallow greenhouse farmlands: case study in southeastern hungary},
	url = {https://m2.mtmt.hu/api/publication/34094864},
	author = {Saadu, Ibrahim and Farsang, Andrea and Kiss, Tímea},
	doi = {10.1186/s12302-023-00777-6},
	journal-iso = {ENVIRON SCI EUR},
	journal = {ENVIRONMENTAL SCIENCES EUROPE},
	volume = {35},
	unique-id = {34094864},
	issn = {2190-4707},
	year = {2023},
	eissn = {2190-4715},
	orcid-numbers = {Farsang, Andrea/0000-0002-7873-5256; Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:34093571,
	title = {High spatiotemporal resolution analysis on suspended sediment and microplastic transport of a lowland river},
	url = {https://m2.mtmt.hu/api/publication/34093571},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Balla, Alexia and Kiss, Tímea},
	doi = {10.1016/j.scitotenv.2023.166188},
	journal-iso = {SCI TOTAL ENVIRON},
	journal = {SCIENCE OF THE TOTAL ENVIRONMENT},
	volume = {902},
	unique-id = {34093571},
	issn = {0048-9697},
	year = {2023},
	eissn = {1879-1026},
	orcid-numbers = {Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:34007585,
	title = {The Role of Past Climatic Variability in Fluvial Terrace Formation, a Case Study from River Mureş (Maros), Romania},
	url = {https://m2.mtmt.hu/api/publication/34007585},
	author = {Bartyik, Tamás and Urdea, P and Kiss, Tímea and Hegyi, A and Sipos, György},
	doi = {10.3390/quat6020035},
	journal-iso = {Quaternary},
	journal = {QUATERNARY},
	volume = {6},
	unique-id = {34007585},
	issn = {2571-550X},
	abstract = {Fluvial terrace formation is a complex process governed by the interplay of climatic and tectonic forcings. From a climatic perspective, an incision is usually related to climatic transitions, while valley aggradation is attributed to glacial periods. We have reconstructed the formation of Late Pleistocene fluvial terraces along the middle, mountainous section of a temperate zone river (Mureş/Maros) in order to identify the roles of different climatic periods and potential vertical displacement in terrace development. Investigations were based on two profiles representing two different terrace levels. The profiles were subjected to sedimentological and detailed geochronological analyses using optically stimulated luminescence (OSL). The results indicated that the investigated terraces represent different incision events coinciding with climatic transition periods. However, a joint MIS 3 valley aggradation period can be identified at both of them. Thus, the relatively mild but highly variable climate of the MIS 3 facilitated sediment mobilization from upland catchments. On the other hand, there is no evidence of aggradation under the cold and stable climate of MIS 2. However, the tectonic setting favours incision at the site. Based on our results, we concluded that the timing of the main events was controlled primarily by climatic forcing. The terrace formation model recognised might also be applied at other rivers in the region.},
	year = {2023},
	orcid-numbers = {Kiss, Tímea/0000-0002-2597-5176; Hegyi, A/0000-0002-5373-912X; Sipos, György/0000-0001-6224-2361}
}

@article{MTMT:33879836,
	title = {Mapping subsurface defects and surface deformation along the artificial levee of the Lower Tisza River, Hungary},
	url = {https://m2.mtmt.hu/api/publication/33879836},
	author = {Sheishah, Diaa Elsayed Hamed Abdallah Hamed and Kiss, Tímea and Borza, T. and Fiala, K. and Kozák, P. and Abdelsamei, Enas and Tóth, Csaba and Grenerczy, G. and Páll, D.G. and Sipos, György},
	doi = {10.1007/s11069-023-05922-1},
	journal-iso = {NAT HAZARDS},
	journal = {NATURAL HAZARDS},
	volume = {117},
	unique-id = {33879836},
	issn = {0921-030X},
	year = {2023},
	eissn = {1573-0840},
	pages = {1647-1671},
	orcid-numbers = {Kiss, Tímea/0000-0002-2597-5176; Tóth, Csaba/0000-0001-5065-5177; Sipos, György/0000-0001-6224-2361}
}

@article{MTMT:33862864,
	title = {Mountains of plastic: Mismanaged plastic waste along the Carpathian watercourses},
	url = {https://m2.mtmt.hu/api/publication/33862864},
	author = {Liro, Maciej and Zielonka, Anna and van Emmerik, Tim H.M. and Grodzińska-Jurczak, Małgorzata and Liro, Justyna and Kiss, Tímea and Mihai, Florin-Constantin},
	doi = {10.1016/j.scitotenv.2023.164058},
	journal-iso = {SCI TOTAL ENVIRON},
	journal = {SCIENCE OF THE TOTAL ENVIRONMENT},
	volume = {888},
	unique-id = {33862864},
	issn = {0048-9697},
	year = {2023},
	eissn = {1879-1026},
	orcid-numbers = {Kiss, Tímea/0000-0002-2597-5176}
}

@article{MTMT:33785558,
	title = {Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery},
	url = {https://m2.mtmt.hu/api/publication/33785558},
	author = {Mohsen Abdelsadek Metwaly, Ahmed and Kiss, Tímea and Kovács, Ferenc},
	doi = {10.1007/s11356-023-27068-0},
	journal-iso = {ENVIRON SCI POLLUT R},
	journal = {ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH},
	volume = {30},
	unique-id = {33785558},
	issn = {0944-1344},
	abstract = {Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary.},
	year = {2023},
	eissn = {1614-7499},
	pages = {67742-67757},
	orcid-numbers = {Kiss, Tímea/0000-0002-2597-5176; Kovács, Ferenc/0000-0001-7944-8921}
}