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001-es BibID:	BIBFORM119435
035-os BibID:	(Scopus)85187466751 (WoS)001181164800001
Első szerző:	Phinzi, Kwanele
Cím:	Predictive machine learning for gully susceptibility modeling with geo?environmental covariates: main drivers, model performance, and computational efciency / Kwanele Phinzi, Szilárd Szabó
Dátum:	2024
ISSN:	0921-030X
Megjegyzések:	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, F1-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.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Gully erosion Machine learning Predictive modeling Accuracy Computational efficiency Geo-environmental predictors
Megjelenés:	Natural Hazards. - [Epub ahead of print] : - (2024), p. -. -
További szerzők:	Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	K 138079 Egyéb KKP144068 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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