Comparison of error and uncertainty of decision tree and learning vector quantization models for predicting soil classes in areas with low altitude variations-Reference-Cited by-同舟云学术

Comparison of error and uncertainty of decision tree and learning vector quantization models for predicting soil classes in areas with low altitude variations

Published:2020-08 Issue: Volume:191 Page:104581
ISSN:0341-8162
Container-title:CATENA
language:en
Short-container-title:CATENA

Author:

Esfandiarpour-Boroujeni I.,Shamsabadi M. Shahini,Shirani H.,Mosleh Z.,Bagheri Bodaghabadi M.^ORCID,Salehi M.H.

Publisher

Elsevier BV

Subject

Earth-Surface Processes

Reference52 articles.

1. A new fuzzy learning vector quantization method for classification problems based on a granular approach;Amezcua;Granular Comput.,2018

2. Digital soil mapping using artificial neural networks and terrain-related attributes;Bagheri Bodaghabadi;Pedosphere,2015

3. Land use and climate control the spatial distribution of soil types in the grasslands of Inner Mongolia;Barthold;J. Arid Environ.,2013

4. Managing uncertainty in integrated environmental modelling: the UncertWeb framework;Bastin;Environ. Model Soft.,2013

5. A comparison of data-mining techniques in predictive soil mapping;Behrens,2007

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran;Earth Science Informatics;2024-08-23

2. A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas;Land;2024-03-17

3. Improvement of data imbalance for digital soil class mapping in Eastern China;Computers and Electronics in Agriculture;2023-11

4. Application of fractional-order differential and ensemble learning to predict soil organic matter from hyperspectra;Journal of Soils and Sediments;2023-09-09

5. Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service;The International Journal of Advanced Manufacturing Technology;2023-08-23