Szczegóły publikacji
Opis bibliograficzny
Development and optimization of a morphodiversity model for mountainous areas using supervised classification and artificial neural networks / Tomasz BARTUŚ // Scientific Reports [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2045-2322 . — 2026 — vol. 16 art. no. 6009, s. 1-34. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 31-34, Abstr. — Publikacja dostępna online od: 2026-01-22
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166072 |
|---|---|
| Data dodania do BaDAP | 2026-03-06 |
| Tekst źródłowy | URL |
| DOI | 10.1038/s41598-026-36326-3 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Scientific Reports |
Abstract
Morphodiversity assessments in mountainous landscapes represent an important tool for geoconservation. Particularly useful are the Raster Continuous Morphodiversity (RCM) models, which are based on continuous variables and measures of pixel variability within statistical zones. Traditional RCM models of the Aggregating Ratings (AR) type define morphodiversity as the sum of standardized partial criteria; however, their drawback lies in the redundant aggregation of variability, which leads to evaluation errors. To mitigate this issue, a new RCM model was developed, based on Supervised Classification (SC) and Artificial Neural Networks (ANN), which accounts for interdependencies between variables and eliminates low-informative ones. This study presents the results of RCM modeling for the Pieniny Mountains (southern Poland) using the RCMSC−ANN and the improved RCMSC−ANN−M models. Variable reduction was performed using the Global Sensitivity Analysis (GSA) method in its Backward Analysis (BA) and Backward Stepwise Analysis (BSA) variants, resulting in simplified models. Comparisons with the SC-ANNVSR−9−BS and RCMSDcm models showed that RCMSC−ANN−M produces more reliable evaluations and can be recommended for testing in other mountainous areas. The high correlation coefficients (0.96–0.98) between full and reduced sets of criteria confirm the effectiveness and efficiency of the BA and BSA procedures.
