Journal Article10.1016/J.GEODERMA.2017.03.013
Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates
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TL;DR: In this article, the authors evaluated the extent to which digital elevation models (DEM) derivatives and machine learning algorithms (k-nearest neighbor, support vector machine, decision tree (DT) and random forest) can be used for predicting the location and extent of salt-affected areas within the Vaalharts and Breede River irrigation schemes of South Africa.
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About: This article is published in Geoderma. The article was published on 01 Aug 2017. The article focuses on the topics: Geomorphometry & Shuttle Radar Topography Mission.
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Citations
Machine learning for digital soil mapping: Applications, challenges and suggested solutions
TL;DR: For future developments, ML could incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to couple model prediction with pedological explanation and understanding of the underlying soil processes.
Hydrologically Informed Machine Learning for Rainfall-Runoff Modeling: A Genetic Programming-Based Toolkit for Automatic Model Induction
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Multi-algorithm comparison for predicting soil salinity
TL;DR: In this paper, the authors compared thirteen popular and non-popular algorithms and their performances following four criteria in predicting soil salinity from environmental covariates from Kuqa Oasis from Xinjiang, China.
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Comparison between geostatistical and machine learning models as predictors of topsoil organic carbon with a focus on local uncertainty estimation
TL;DR: This work compares geostatistical techniques, ML methods and hybrid methods, e.g., regression kriging, in terms of not only their overall accuracy but also their precision in providing useful confidence intervals at unsampled locations to provide clear application guidelines for future mapping exercises.
107
Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine
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TL;DR: With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated cl....
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