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...

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Vydáno v:	Journal of environmental management Ročník 284; s. 112015
Hlavní autoři:	Chen, Wei, Lei, Xinxiang, Chakrabortty, Rabin, Chandra Pal, Subodh, Sahana, Mehebub, Janizadeh, Saeid
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 15.04.2021
Témata:	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
On-line přístup:	Získat plný text
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