Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility

The objective of this study is to assess the gully head-cut erosion susceptibility and identify gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area has been greatly influenced by several head-cut gullies due to unusual climatic factors and human induced activit...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of environmental management Vol. 284; p. 112015
Main Authors:	Chen, Wei, Lei, Xinxiang, Chakrabortty, Rabin, Chandra Pal, Subodh, Sahana, Mehebub, Janizadeh, Saeid
Format:	Journal Article
Language:	English
Published:	England Elsevier Ltd 15.04.2021
Subjects:	altitude Climatic factors Conservation of Natural Resources data collection Deep Learning environmental management gully erosion Gully head-cut erosion Humans Internet inventories Iran issues and policy land use Machine Learning morphometry prediction Predictive accuracy regression analysis Soil Soil conservation watersheds Iran Climatic factors Predictive accuracy Gully head-cut erosion Soil conservation
ISSN:	0301-4797, 1095-8630, 1095-8630
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!