Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.
Autor: | Nasiri Khiavi A; Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, 46414-356, Iran., Vafakhah M; Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, 46414-356, Iran. vafakhah@modares.ac.ir. |
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Jazyk: | angličtina |
Zdroj: | Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Aug; Vol. 31 (40), pp. 52740-52757. Date of Electronic Publication: 2024 Aug 19. |
DOI: | 10.1007/s11356-024-34691-y |
Abstrakt: | This study was carried out with the aim of applying Condorcet and Borda scoring algorithms based on Game Theory (GT) to determine flood points and Flood Susceptibility Mapping (FSM) based on Machine Learning Algorithms (MLA) including Random Forest (RF), Support Vector Regression (SVR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in the Cheshmeh-Kileh watershed, Iran. Therefore, first, FS conditioning factors including Aspect (A (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.) |
Databáze: | MEDLINE |
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