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Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility.

, , , , , , , and . Remote. Sens., 12 (20): 3284 (2020)

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Ground Subsidence Hazard Analysis in an Abandoned Underground Coal Mine Area using Probabisltic and Logistic Regression Models., , and . IGARSS, page 1549-1552. IEEE, (2006)Landslide Hazard Analysis for Development of an Early Warning System., , , and . Konecny, M., Zlatanova, S., Bandrova, T., Friedmannova, L. (Ed.s): Cartography and Geoinformatics for Early Warning and Emergency Management: Towards Better Solutions. Masaryk University, Brno, (2009)Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility., , , , , , , and . Remote. Sens., 12 (20): 3284 (2020)Integration of mineral potential maps from various geospatial models.. COM.Geo, page 52:1-52:2. ACM, (2011)Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility., , , , , , , and . Remote. Sens., 12 (22): 3675 (2020)Development of novel optimized deep learning algorithms for wildfire modeling: A case study of Maui, Hawai'i., , , , , , and . Eng. Appl. Artif. Intell., (2023)Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility., , , , , , and . Remote. Sens., 12 (20): 3389 (2020)Mineral Potential Assessment of Sedimentary Deposit using Frequency Ratio and Logistic Regression of Gangreung Area, Korea., , and . IGARSS, page 1576-1579. IEEE, (2006)Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea., , , and . Remote Sensing, 10 (10): 1545 (2018)Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility., , , , , , , and . Remote. Sens., 12 (17): 2833 (2020)