@CONFERENCE{MTMT:34799801, title = {Evaluating the efficacy of multitemporal TLS and UAS surveys for quantifying wind erosion magnitudes of sand dune topography}, url = {https://m2.mtmt.hu/api/publication/34799801}, author = {Bertalan, László and Négyesi, Gábor and Szabó, Gergely and Túri, Zoltán and Szabó, Szilárd}, booktitle = {EGU General Assembly 2024 abstract kötet}, unique-id = {34799801}, year = {2024}, orcid-numbers = {Bertalan, László/0000-0002-5963-2710; Szabó, Szilárd/0000-0002-2670-7384} } @article{MTMT:34742919, title = {Predictive machine learning for gully susceptibility modeling with geo-environmental covariates: main drivers, model performance, and computational efficiency}, url = {https://m2.mtmt.hu/api/publication/34742919}, author = {Phinzi, Kwanele and Szabó, Szilárd}, doi = {10.1007/s11069-024-06481-9}, journal-iso = {NAT HAZARDS}, journal = {NATURAL HAZARDS}, unique-id = {34742919}, issn = {0921-030X}, abstract = {Currently, machine learning (ML) based gully susceptibility prediction is a rapidly expanding research area. However, when assessing the predictive performance of ML models, previous research frequently overlooked the critical component of computational efficiency in favor of accuracy. This study aimed to evaluate and compare the predictive performance of six commonly used algorithms in gully susceptibility modeling. Artificial neural networks (ANN), partial least squares, regularized discriminant analysis, random forest (RF), stochastic gradient boosting, and support vector machine (SVM) were applied. The comparison was conducted under three scenarios of input feature set sizes: small (six features), medium (twelve features), and large (sixteen features). Results indicated that SVM was the most efficient algorithm with a medium-sized feature set, outperforming other algorithms across all overall accuracy (OA) metrics (OA = 0.898, F 1-score = 0.897) and required a relatively short computation time (< 1 min). Conversely, ensemble-based algorithms, mainly RF, required a larger feature set to reach optimal accuracy and were computationally demanding, taking about 15 min to compute. ANN also showed sensitivity to the number of input features, but unlike RF, its accuracy consistently decreased with larger feature sets. Among geo-environmental covariates, NDVI, followed by elevation, TWI, population density, SPI, and LULC, were critical for gully susceptibility modeling. Therefore, using SVM and involving these covariates in gully susceptibility modeling in similar environmental settings is strongly suggested to ensure higher accuracy and minimal computation time.}, keywords = {machine learning; Computational efficiency; Predictive modeling; gully erosion; Geo-environmental predictors}, year = {2024}, eissn = {1573-0840}, orcid-numbers = {Szabó, Szilárd/0000-0002-2670-7384} } @article{MTMT:34741333, title = {Classification Assessment Tool: A program to measure the uncertainty of classification models in terms of class-level metrics}, url = {https://m2.mtmt.hu/api/publication/34741333}, author = {Szabó, Szilárd and Holb, Imre and Abriha-Molnár, Vanda Éva and Szatmári, Gábor and Singh, Sudhir Kumar and Abriha, Dávid}, doi = {10.1016/j.asoc.2024.111468}, journal-iso = {APPL SOFT COMPUT}, journal = {APPLIED SOFT COMPUTING}, volume = {155}, unique-id = {34741333}, issn = {1568-4946}, abstract = {Accuracy assessments are important steps of classifications and get higher relevance with the soar of machine and deep learning techniques. We provided a method for quick model evaluations with several options: calculate the class level accuracy metrics for as many models and classes as needed; calculate model stability using random subsets of the testing data. The outputs are single calculations, summaries of the repetitions, and/or all accuracy results per repetitions. Using the application, we demonstrated the possibilities of the function and analyzed the accuracies of three experiments. We found that some popular metrics, the binary Overall Accuracy, Sensitivity, Precision, and Specificity, as well as ROC curve, can provide false results when the true negative cases dominate. F1-score, Intersection over Union and the Matthews correlation coefficient were reliable in all experiments. Medians and interquartile ranges (IQR) of the repeated sampling from the testing dataset showed that IQR were small when a model was almost perfect or completely unacceptable; thus, IQR reflected the model stability, reproducibility. We found that there were no general, statistically justified relationship with the median and IQR, furthermore, correlations of accuracy metrics varied by experiments, too. Accordingly, a multi-metric evaluation is suggested instead of a single metric.}, keywords = {REPETITION; Python; model evaluation; Model stability}, year = {2024}, eissn = {1872-9681}, orcid-numbers = {Szabó, Szilárd/0000-0002-2670-7384; Szatmári, Gábor/0000-0003-3201-598X} } @article{MTMT:34476689, title = {Impact of the El Niño on Fire Dynamics on the African Continent}, url = {https://m2.mtmt.hu/api/publication/34476689}, author = {de Oliveira-Júnior, José Francisco and Mendes, David and Szabó, Szilárd and Singh, Sudhir Kumar and Jamjareegulgarn, Punyawi and Cardoso, Kelvy Rosalvo Alencar and Bertalan, László and da Silva, Marcos Vinicius and da Rosa Ferraz Jardim, Alexandre Maniçoba and da Silva, Jhon Lennon Bezerra and Lyra, Gustavo Bastos and Abreu, Marcel Carvalho and Filho, Washington Luiz Félix Correia and de Sousa, Amaury and de Barros Santiago, Dimas and da Silva Santos, Iwldson Guilherme and Maksudovna, Vafaeva Khristina}, doi = {10.1007/s41748-023-00363-z}, journal-iso = {EARTH SYST ENVIRON}, journal = {EARTH SYSTEMS AND ENVIRONMENT}, volume = {-}, unique-id = {34476689}, issn = {2509-9426}, abstract = {Several studies investigated the occurrence of fires in Africa with numerical modeling or applied statistics; however, only a few studies focused on the influence of El Niño on the fire risk using a coupled model. The study aimed to assess the influence of El Niño on wildfire dynamics in Africa using the SPEEDY-HYCOM model. El Niño events in the Eastern Tropical Pacific were classified via sea surface temperature (SST) anomaly based on a predefined climatology between 1961 and 2020 for the entire time series of SST, obtaining linear anomalies. The time series of the SST anomalies was created for the region between 5° N and 5° S and 110° W and 170° W. The events were defined in three consecutive 3-month periods as weak, moderate, and strong El Niño conditions. The Meteorological Fire Danger Index (MFDI) was applied to detect fire hazards. The MFDI simulated by the SPEEDY-HYCOM model for three El Niño categories across different lagged months revealed relevant distinctions among the categories. In the case of ‘Weak’, the maximum variability of fire risk observed at time lags (0, -3, -6, and -9 months) was primarily in Congo, Gabon, and Madagascar. The ‘Moderate’ pattern had similar characteristics to ‘Weak’ except for the lag-6 months and its occurrence in the equatorial zone of Africa. ‘Strong’ showed a remarkable impact in East Africa, resulting in high fire risk, regardless of time lags. Precipitation and evaporation simulations (SPEEDY-HYCOM) indicated that El Niño categories in Africa need particular attention in the central, southern, and southeastern regions emphasizing the significance of lag-0 and lag-6 (evaporation) as well as lag-0, lag-6, and lag-9 (precipitation). The SPEEDY-HYCOM coupled model in conjunction with the MFDI was efficient in assessing climate variabilities in Africa during El Niño events. This model allows the analysis and prediction of wildfire risks based on El Niño events, providing crucial information for wildfire management and prevention. Its simulations uncover significant variations in risks among different El Niño categories and lagged months, contributing to the understanding and mitigation of this environmental challenge.}, keywords = {fire risk; Coupled modelling; El Nino categories; SPEEDY-HYCOM model}, year = {2024}, eissn = {2509-9434}, orcid-numbers = {Szabó, Szilárd/0000-0002-2670-7384; Bertalan, László/0000-0002-5963-2710} } @article{MTMT:34396433, title = {Assessment of multi-source satellite products using hydrological modelling approach}, url = {https://m2.mtmt.hu/api/publication/34396433}, author = {Mahanta, Aiswarya Rani and Rawat, Kishan Singh and Kumar, Nirmal and Szabó, Szilárd and Srivastava, Prashant K. and Singh, Sudhir Kumar}, doi = {10.1016/j.pce.2023.103507}, journal-iso = {PHYS CHEM EARTH (2002-)}, journal = {PHYSICS AND CHEMISTRY OF THE EARTH (2002-)}, volume = {133}, unique-id = {34396433}, issn = {1474-7065}, abstract = {Multi-source satellite products performance evaluation for varied geographical locations aids in quantification of hydrological variables and is useful in the strategy making and conservation of the hydrological resources available in a basin. The work was focused on assessing utility of multi-source satellite datasets to obtain the estimation of hydrologic variables and provide solution for areas that are poorly gauged or un-gauged. Assessment of the multi-source-satellite products was performed for the poorly gauged river basin with the help of SWAT concerning the Palar River basin, India. We analysed time series at the monthly, seasonal, and annual scales to quantify surface runoff, water yield, ET, & PET at the calibration site and for the entire basin for the period 2003 to 2021, depending on the common period of availability of all the data sets. SWAT model estimated highest monthly water yield during November–December, with annual water yield being maximum (220 mm in 2010) and average (99 mm), which can be used to understand water resources for irrigation, drinking aspects, and net storage. Average monthly surface runoff patterns were similar for SWAT, TerraClimate, and FLDAS. The FLDAS and SWAT simulated surface runoff show a resemblance in pattern and magnitude for the monthly and annual time series of the average basin scenario. The monthly PET obtained from SWAT and ERA-5 show a similar pattern for the entire basin and at the calibration site. The ET derived from satellite observation has over-predicted the model output at both the calibration site and entire basin}, keywords = {MODIS; Water yield; SWAT; MERRA-2; FLDAS; ERA-5}, year = {2024}, eissn = {1873-5193}, orcid-numbers = {Szabó, Szilárd/0000-0002-2670-7384} } @article{MTMT:34148822, title = {Assessment of the environmental kuznets curve within EU-27: Steps toward environmental sustainability (1990–2019)}, url = {https://m2.mtmt.hu/api/publication/34148822}, author = {Mohammed, Safwan and Gill, Abid Rashid and Ghosal, Kaushik and Al-Dalahmeh, Main and Alsafadi, Karam and Szabó, Szilárd and Oláh, Judit and Alkerdi, Ali and Ocwa, Akasairi and Harsányi, Endre}, doi = {10.1016/j.ese.2023.100312}, journal-iso = {Environmental Science and Ecotechnology}, journal = {Environmental Science and Ecotechnology}, volume = {18}, unique-id = {34148822}, issn = {2666-4984}, year = {2024}, eissn = {2666-4984}, orcid-numbers = {Gill, Abid Rashid/0000-0003-1961-5139; Ghosal, Kaushik/0000-0003-3235-0278; Alsafadi, Karam/0000-0001-8925-7918; Szabó, Szilárd/0000-0002-2670-7384; Oláh, Judit/0000-0003-2247-1711} } @article{MTMT:34092358, title = {Deep learning‐based training data augmentation combined with post‐classification improves the classification accuracy for dominant and scattered invasive forest tree species}, url = {https://m2.mtmt.hu/api/publication/34092358}, author = {Likó, Szilárd Balázs and Holb, Imre and Oláh, Viktor and Burai, Péter and Szabó, Szilárd}, doi = {10.1002/rse2.365}, journal-iso = {REMOTE SENS ECOL CON}, journal = {REMOTE SENSING IN ECOLOGY AND CONSERVATION}, unique-id = {34092358}, abstract = {Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning‐based training data augmentation (TDA) and post‐classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post‐classification with segmentation improved the total accuracy to 86.2%. The class‐level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future.}, keywords = {Black locust; Multiresolution segmentation; Convolutional-Neural-Network; RANDOM-FOREST; Support-vector-machine; ailanthus}, year = {2024}, eissn = {2056-3485}, orcid-numbers = {Oláh, Viktor/0000-0001-5410-5914; Szabó, Szilárd/0000-0002-2670-7384} } @{MTMT:34735335, title = {How Bad Volcano-Clastic Badlands Actually Are?}, url = {https://m2.mtmt.hu/api/publication/34735335}, author = {N., Antić and M., Kašanin-Grubin and Bertalan, László and V., Gajić and L., Kaluđerović and N., Mijatović and B., Jovančićević}, booktitle = {23rd European Meeting on Environmental Chemistry, EMEC23, Book of abstracts}, unique-id = {34735335}, year = {2023}, pages = {22}, orcid-numbers = {Bertalan, László/0000-0002-5963-2710} } @article{MTMT:34575289, title = {A felszínborítás változása a karcsai Karcsa-tó környezetében 1966–2020 között}, url = {https://m2.mtmt.hu/api/publication/34575289}, author = {Nagy, Bálint and Phinzi, Kwanele}, doi = {10.32643/f k.147.2.6}, journal-iso = {FÖLDRAJZI KÖZLEMÉNYEK}, journal = {FÖLDRAJZI KÖZLEMÉNYEK}, volume = {147}, unique-id = {34575289}, issn = {0015-5411}, year = {2023}, pages = {143-156} } @CONFERENCE{MTMT:34524988, title = {Badlands involcano-clastic rocks: examplesfrom Serbia and Hungary}, url = {https://m2.mtmt.hu/api/publication/34524988}, author = {Antić, Nevena and Bertalan, László and Stefanović, Milica and Kašanin-Grubin, Milica}, booktitle = {EUGEO}, unique-id = {34524988}, abstract = {Badlands can develop under different climatic conditions ranging from arid to humid on materials that have a specific combination of physico-chemical properties depending on their mineralogical composition. Mostly these materials are fine-grained terrigenous, lacustrine or marine sediments of different age. However, badlands can also form in volcano-clasitc materials, and Cappadocia badlands in Turkey is the most prominent example. Less known in the literature are two sites also developed in this type of sediments: Đavolja Varoš, on Radan Moutain in SE Serbia and the Kazár badlands in NW Hungary. The Đavolja Varoš badlands, 0.7 km2 in size is formed by the intensive development of rills and gullies on slopes built from thick volcano-clastic material. The initial relief is reduced only to sharp ridges between adjacent gullies. This badland is developed in dacito-andesitic poorly-consolidated poorly-sorted tuffs. The weathering processes are intense and governed by high intensity precipitation and prolonged drying periods. The reddish earth pyramids built of these erodible materials are protected by the cap rock. Loss of balance and fall of the protective cap rock accelerates the erosion. The smaller Kazár badlands covering the area of 1ha are developed in rhyolitic poorly-consolidated highly porous tuffs. Rills and gullies are the dominant geomorphic processes and the weathering is dominated by freeze-thaw processes. At the Kazár badlands earth pyramids are not protected with the cap rock and weathering, disintegration and sheet wash erosion intensively shape the landscape. Although the materials differ slightly in composition, both being poorly sorted, clay-size rich materials make them sensitive to erosion, proving once more the importance of material composition, in this case namely grain size including sorting and mineralogical composition, for badlands development and future evolution.}, year = {2023}, orcid-numbers = {Bertalan, László/0000-0002-5963-2710} }