Predicting gully formation: an approach for assessing susceptibility and future risk

DOI10.1111/nrm.12409MaRDI QIDQ6669126

Leila Goli Mokhtari, Nadiya Baghaei Nejad, Ali Beheshti

Publication date: 22 January 2025

Published in: Natural Resource Modeling (Search for Journal in Brave)

zbMATH Keywords

machine learningensemble learninginformation gain ratiogully predictionKalshour basin

Mathematics Subject Classification ID

Environmental economics (natural resource models, harvesting, pollution, etc.) (91B76)

Cites Work

• Bagging predictors
• Machine learning feature selection methods for landslide susceptibility mapping
• A decision-theoretic generalization of on-line learning and an application to boosting
• Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Principal component analysis.
• Improved boosting algorithms using confidence-rated predictions
• Reliable asynchronous sampled-data filtering of T-S fuzzy uncertain delayed neural networks with stochastic switched topologies
• The Measurement of Observer Agreement for Categorical Data
• Extremely randomized trees

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