Predicting active-layer soil thickness using topographic variables at a small watershed scale
出版年份 2017 全文链接
标题
Predicting active-layer soil thickness using topographic variables at a small watershed scale
作者
关键词
Agricultural soil science, Terrain, Support vector machines, Linear regression analysis, Topography, Kernel functions, Machine learning, Soil ecology
出版物
PLoS One
Volume 12, Issue 9, Pages e0183742
出版商
Public Library of Science (PLoS)
发表日期
2017-09-07
DOI
10.1371/journal.pone.0183742
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Modelling soil thickness in the critical zone for Southern British Columbia
- (2016) Christopher Scarpone et al. GEODERMA
- Predicting Soil Infiltration and Horizon Thickness for a Large-Scale Water Balance Model in an Arid Environment
- (2016) Tadaomi Saito et al. Water
- A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape
- (2015) Kennedy Were et al. ECOLOGICAL INDICATORS
- Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
- (2015) V. Rodriguez-Galiano et al. ORE GEOLOGY REVIEWS
- Relationship between soil depth and terrain attributes in karst region in Southwest China
- (2014) Qiyong Yang et al. JOURNAL OF SOILS AND SEDIMENTS
- Distribution and changes of active layer thickness (ALT) and soil temperature (TTOP) in the source area of the Yellow River using the GIPL model
- (2014) DongLiang Luo et al. Science China-Earth Sciences
- Active-Layer Thickness across Alaska: Comparing Observation-Based Estimates with CMIP5 Earth System Model Predictions
- (2014) Umakant Mishra et al. SOIL SCIENCE SOCIETY OF AMERICA JOURNAL
- Calculation and control of flow path length in superelevation sections
- (2013) Zhuo Zhang et al. INTELLIGENT AUTOMATION AND SOFT COMPUTING
- Relationships between soil depth and terrain attributes in a semi arid hilly region in western Iran
- (2013) Abdolmohammad Mehnatkesh et al. Journal of Mountain Science
- Improving regional soil carbon inventories: Combining the IPCC carbon inventory method with regression kriging
- (2012) Umakant Mishra et al. GEODERMA
- Influences of spatial distribution of soil thickness on shallow landslide prediction
- (2011) Jui-Yi Ho et al. ENGINEERING GEOLOGY
- Prediction of Soil Depth from Digital Terrain Data by Integrating Statistical and Visual Approaches
- (2010) F.M. ZIADAT PEDOSPHERE
- Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem
- (2010) Martin Wiesmeier et al. PLANT AND SOIL
- Prediction of soil depth using environmental variables in an anthropogenic landscape, a case study in the Western Ghats of Kerala, India
- (2009) Sekhar L. Kuriakose et al. CATENA
- Spatial prediction of soil properties in temperate mountain regions using support vector regression
- (2009) Cristiano Ballabio GEODERMA
- Test of statistical means for the extrapolation of soil depth point information using overlays of spatial environmental data and bootstrapping techniques
- (2009) Helen E. Dahlke et al. HYDROLOGICAL PROCESSES
- Prediction of landslide occurrence based on slope-instability analysis and hydrological model simulation
- (2009) Kwan Tun Lee et al. JOURNAL OF HYDROLOGY
- Modeling soil depth from topographic and land cover attributes
- (2009) Teklu K. Tesfa et al. WATER RESOURCES RESEARCH
- Small scale digital soil mapping in Southeastern Kenya
- (2008) Alejandra Mora-Vallejo et al. CATENA
- Soil organic carbon concentrations and stocks on Barro Colorado Island — Digital soil mapping using Random Forests analysis
- (2008) R. Grimm et al. GEODERMA
- Distribution and characteristics of soil thickness and effects upon water storage in forested areas of Cambodia
- (2008) Yasuhiro Ohnuki et al. HYDROLOGICAL PROCESSES
- A steady-state analytical slope stability model for complex hillslopes
- (2007) Ali Talebi et al. HYDROLOGICAL PROCESSES
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started