@article{MTMT:34785080,
	title = {Geochemical and sedimentological analyses on the Romanian Sphagnum peat bog Tăul fără fund},
	url = {https://m2.mtmt.hu/api/publication/34785080},
	author = {Vári, Tamás Zsolt and Sümegi, Pál},
	doi = {10.19189/MaP.2023.OMB.Sc.2308958},
	journal-iso = {MIRES PEAT},
	journal = {MIRES AND PEAT},
	volume = {31},
	unique-id = {34785080},
	issn = {1819-754X},
	keywords = {Sedimentology; Quaternary; Geochemistry; Holocene; AMS radiocarbon dating; Romania},
	year = {2024},
	eissn = {1819-754X},
	pages = {1-16},
	orcid-numbers = {Vári, Tamás Zsolt/0000-0002-1763-614X; Sümegi, Pál/0000-0003-1755-4440}
}

@article{MTMT:34539323,
	title = {Integrating the Sentinel-1, Sentinel-2 and Topographic data into soybean yield modelling using Machine Learning},
	url = {https://m2.mtmt.hu/api/publication/34539323},
	author = {Amankulova, Khilola and Farmonov, Nizom and Omonov, Khasan and Abdurakhimova, Mokhigul and Mucsi, László},
	doi = {10.1016/j.asr.2024.01.040},
	journal-iso = {ADV SPACE RES},
	journal = {ADVANCES IN SPACE RESEARCH},
	volume = {73},
	unique-id = {34539323},
	issn = {0273-1177},
	year = {2024},
	eissn = {1879-1948},
	pages = {4052-4066},
	orcid-numbers = {Amankulova, Khilola/0000-0001-6562-5616; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:34427009,
	title = {Late Quaternary Paleoecology and Environmental History of the Hortobágy, an Alkaline Steppe in Central Europe},
	url = {https://m2.mtmt.hu/api/publication/34427009},
	author = {Szilágyi, Gábor and Gulyás, Sándor and Vári, Tamás Zsolt and Sümegi, Pál},
	doi = {10.3390/d16010067},
	journal-iso = {DIVERSITY-BASEL},
	journal = {DIVERSITY (BASEL)},
	volume = {16},
	unique-id = {34427009},
	year = {2024},
	eissn = {1424-2818},
	pages = {67},
	orcid-numbers = {Gulyás, Sándor/0000-0002-3384-2381; Vári, Tamás Zsolt/0000-0002-1763-614X; Sümegi, Pál/0000-0003-1755-4440}
}

@mastersthesis{MTMT:34656363,
	title = {Az okosvárostól az okosfalvakig: a smart city koncepció alkalmazhatóságának néhány földrajzi aspektusa Magyarországon},
	url = {https://m2.mtmt.hu/api/publication/34656363},
	author = {Szalai, Ádám},
	unique-id = {34656363},
	keywords = {városfejlesztés; okos falu; smart village; okos város koncepció; smart city},
	year = {2023},
	orcid-numbers = {Szalai, Ádám/0000-0002-8786-0470}
}

@{MTMT:34571144,
	title = {Spatio-temporal variability of the connection between spatial characteristics of the transportation networks and PM10 air pollution: a European scale analysis},
	url = {https://m2.mtmt.hu/api/publication/34571144},
	author = {SOHRAB, SEYEDEH MEHRMANZAR and Csikós, Nándor and Szilassi, Péter},
	booktitle = {Natural Hazards and Climate Change - conference and workshop for identifying and tackling challenges together},
	unique-id = {34571144},
	year = {2023},
	pages = {36-36},
	orcid-numbers = {SOHRAB, SEYEDEH MEHRMANZAR/0000-0002-2397-3425; Csikós, Nándor/0000-0002-7395-7298; Szilassi, Péter/0000-0003-0051-6739}
}

@{MTMT:34570957,
	title = {Seasonal Variability in PM10 Concentrations: A European-Scale Analysis of Urban and Suburban Land Cover Influences.},
	url = {https://m2.mtmt.hu/api/publication/34570957},
	author = {SOHRAB, SEYEDEH MEHRMANZAR and Csikós, Nándor and Szilassi, Péter},
	booktitle = {International Conference Natural Resources and Environmental Risks: Towards a Sustainable Future},
	unique-id = {34570957},
	year = {2023},
	pages = {59-59},
	orcid-numbers = {SOHRAB, SEYEDEH MEHRMANZAR/0000-0002-2397-3425; Csikós, Nándor/0000-0002-7395-7298; Szilassi, Péter/0000-0003-0051-6739}
}

@article{MTMT:34506642,
	title = {Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate},
	url = {https://m2.mtmt.hu/api/publication/34506642},
	author = {Rosiani, Diyah and Gibral Walay, Muhamad and Rahalintar, Pradini and Candra, Arya Dwi and Sofyan, Akhmad and Arison Haratua, Yesaya},
	doi = {10.18517/ijaseit.13.6.19399},
	journal-iso = {INT J ADV SCI ENG INFORM TECHNOL},
	journal = {INTERNATIONAL JOURNAL ON ADVANCED SCIENCE, ENGINEERING AND INFORMATION TECHNOLOGY},
	volume = {13},
	unique-id = {34506642},
	issn = {2088-5334},
	abstract = {The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates.},
	year = {2023},
	eissn = {2460-6952},
	pages = {2338-2344}
}

@article{MTMT:34500549,
	title = {Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting},
	url = {https://m2.mtmt.hu/api/publication/34500549},
	author = {Farmonov, Nizom and Amankulova, Khilola and Khan, Shahid Nawaz and Abdurakhimova, Mokhigul and Szatmári, József and Khabiba, Tukhtaeva and Makhliyo, Radjabova and Khodicha, Meiliyeva and Mucsi, László},
	doi = {10.15201/hungeobull.72.4.4},
	journal-iso = {HUNG GEOGR BULL (2009-)},
	journal = {HUNGARIAN GEOGRAPHICAL BULLETIN (2009-)},
	volume = {72},
	unique-id = {34500549},
	issn = {2064-5031},
	abstract = {Crop yield forecasting is critical in modern agriculture to ensure food security, economic stability, and effective resource management. The main goal of this study was to combine historical multisource satellite and environmental datasets with a deep learning (DL) model for soybean yield forecasting in the United States’ Corn Belt. The following Moderate Resolution Imaging Spectroradiometer (MODIS) products were aggregated at the county level. The crop data layer (CDL) in Google Earth Engine (GEE) was used to mask the data so that only soybean pixels were selected. Several machine learning (ML) models were trained by using 5 years of data from 2012 to 2016: random forest (RF), least absolute shrinkable and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and decision tree regression (DTR) as well as DL-based one-dimensional convolutional neural network (1D-CNN). The best model was determined by comparing their performances at forecasting the soybean yield in 2017–2021 at the county scale. The RF model outperformed all other ML models with the lowest RMSE of 0.342 t/ha, followed by XGBoost (0.373 t/ha), DTR (0.437 t/ha), and LASSO (0.452 t/ha) regression. However, the 1D-CNN model showed the highest forecasting accuracy for the 2018 growing season with RMSE of 0.280 t/ha. The developed 1D-CNN model has great potential for crop yield forecasting because it effectively captures temporal dependencies and extracts meaningful input features from sequential data.},
	year = {2023},
	eissn = {2064-5147},
	pages = {383-398},
	orcid-numbers = {Szatmári, József/0000-0002-7896-3363; Mucsi, László/0000-0002-5807-3742}
}

@article{MTMT:34445428,
	title = {Integrated archaeobotanical evidence on the vegetation reconstruction around the tomb of Sultan Suleiman I at Szigetvár (SW Hungary)},
	url = {https://m2.mtmt.hu/api/publication/34445428},
	author = {Torma, Andrea and Náfrádi, Katalin and Törőcsik, Tünde and Sümegi, Pál},
	doi = {10.55023/issn.1786-271X.2023-017},
	journal-iso = {ARCHEOMETRIAI MŰHELY},
	journal = {ARCHEOMETRIAI MŰHELY},
	volume = {20},
	unique-id = {34445428},
	issn = {1786-271X},
	abstract = {During the archaeological excavation of the memorial place (türbe) of the Ottoman sultan Suleiman I, a moat was revealed north of the memorial place in 2015. The moat system was identified by boreholes and excavated in 2015, when 30–30 liter samples were taken from the 250 cm deep moat at 15 cm intervals for archaeobotanical and anthracological analyses. Samples were taken at 10 cm intervals for pollen studies from the archaeological profile of the moat filling. In our publication, based on the previously presented geochronological results, our aim was to reconstruct the vegetation around the memorial tomb of Suleiman, on the basis of archaeobotanical, anthracological and pollen analytical data. We were able to reconstruct ploughed lands (cereal cultivation), vegetable, fruit and vineyards, pasture lands, forest patches and trampled areas related to human activity (settlement). The military census of 1689 indicated similar tract of land structure of the crop production areas. The tomb and the Islamic pilgrimage monastery and pilgrim town (Ottoman name was Türbe kasabası) were demolished from 1692/1693 and divided into agricultural zones, where orchards, arable lands, gardens and vineyards were established.},
	year = {2023},
	pages = {201-226},
	orcid-numbers = {Náfrádi, Katalin/0000-0003-3725-9182; Sümegi, Pál/0000-0003-1755-4440}
}

@{MTMT:34434371,
	title = {Püspökfürdői termálvízhez kötődő refúgium jégkor végi és jelenkori jelentősége},
	url = {https://m2.mtmt.hu/api/publication/34434371},
	author = {Sümegi, Pál and Gulyás, Sándor and Náfrádi, Katalin and Törőcsik, Tünde},
	booktitle = {26. Magyar Őslénytani Vándorgyűlés},
	unique-id = {34434371},
	year = {2023},
	pages = {33-34},
	orcid-numbers = {Sümegi, Pál/0000-0003-1755-4440; Gulyás, Sándor/0000-0002-3384-2381}
}