Szerző szerinti böngészés "Hateffard, Fatemeh"
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Tétel Szabadon hozzáférhető Applicability of machine learning models for predicting soil organic carbon content and bulk density under different soil conditions(2023) Hateffard, Fatemeh; Szatmári, Gábor; Novák, TiborTétel Szabadon hozzáférhető CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran(2021) Hateffard, Fatemeh; Mohammed, Safwan; Alsafadi, Karam; Enaruvbe, Glory O.; Heidari, Ahmad; Abdo, Hazem Ghassan; Rodrigo-Comino, JesúsTétel Szabadon hozzáférhető Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022(2024) Benhizia, Ramzi; Phinzi, Kwanele; Hateffard, Fatemeh; Aib, Haithem; Szabó, GyörgyTétel Szabadon hozzáférhető Evaluating the extrapolation potential of random forest digital soil mapping(2024) Hateffard, Fatemeh; Steinbuch, Luc; Heuvelink, Gerard B.M.Tétel Szabadon hozzáférhető High-Resolution Mapping and Assessment of Salt-Affectedness on Arable Lands by the Combination of Ensemble Learning and Multivariate Geostatistics(2022) Hateffard, Fatemeh; Balog, Kitti; Tóth, Tibor; Mészáros, János; Árvai, Mátyás; Kovács, Zsófia Adrienn; Szűcs,-Vásárhelyi Nóra; Koós, Sándor; László, Péter; Novák, Tibor; Pásztor, László; Szatmári, GáborTétel Szabadon hozzáférhető Using Machine Learning Techniques to Solve Digital Soil Mapping Issues: Spatial Extrapolation and Joint Spatial Modelling of Soil PropertiesHateffard, Fatemeh; Novák, Tibor József; Szatmári, Gábor; Földtudományok Doktori Iskola; Debreceni Egyetem::Természettudományi és Technológiai KarIn this study, I investigated the use of digital soil mapping (DSM) techniques for predicting and mapping soil properties. I reviewed two major challenges in DSM: limited observations in many parts of the world and the complexity of soil systems. To address the first challenge, I explored the extrapolation of soil properties from areas with observations to those without, based on similarities in soil-forming factors. For the second challenge, I combined multivariate geostatistics with machine learning (ML) algorithms to model the spatial interdependence of soil variables. The study included three case studies: comparing ML models for small-scale soil mapping, extrapolating ML models to different areas, and mapping salt-affected soils in Hungary using ensemble ML and multivariate geostatistics. The results showed the effectiveness of RF as the best ML model in the first case study, limitations in extrapolation due to soil-landscape complexity in the second case study, and the efficiency of ensemble ML with high-resolution mapping in the third case study. The study highlights the importance of considering scorpan factors and limitations of ML algorithms when applying DSM techniques for soil property predictions.