Mapping wind erosion hazard with regression-based machine learning algorithms

Abstract Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monoto...

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Autores principales:	Hamid Gholami, Aliakbar Mohammadifar, Dieu Tien Bui, Adrian L. Collins
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6cc90782ff3b44d1a046fcc3ff1da721
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spelling	oai:doaj.org-article:6cc90782ff3b44d1a046fcc3ff1da7212021-12-02T12:33:54ZMapping wind erosion hazard with regression-based machine learning algorithms10.1038/s41598-020-77567-02045-2322https://doaj.org/article/6cc90782ff3b44d1a046fcc3ff1da7212020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77567-0https://doaj.org/toc/2045-2322Abstract Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.Hamid GholamiAliakbar MohammadifarDieu Tien BuiAdrian L. CollinsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-16 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hamid Gholami Aliakbar Mohammadifar Dieu Tien Bui Adrian L. Collins Mapping wind erosion hazard with regression-based machine learning algorithms
description	Abstract Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.
format	article
author	Hamid Gholami Aliakbar Mohammadifar Dieu Tien Bui Adrian L. Collins
author_facet	Hamid Gholami Aliakbar Mohammadifar Dieu Tien Bui Adrian L. Collins
author_sort	Hamid Gholami
title	Mapping wind erosion hazard with regression-based machine learning algorithms
title_short	Mapping wind erosion hazard with regression-based machine learning algorithms
title_full	Mapping wind erosion hazard with regression-based machine learning algorithms
title_fullStr	Mapping wind erosion hazard with regression-based machine learning algorithms
title_full_unstemmed	Mapping wind erosion hazard with regression-based machine learning algorithms
title_sort	mapping wind erosion hazard with regression-based machine learning algorithms
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/6cc90782ff3b44d1a046fcc3ff1da721
work_keys_str_mv	AT hamidgholami mappingwinderosionhazardwithregressionbasedmachinelearningalgorithms AT aliakbarmohammadifar mappingwinderosionhazardwithregressionbasedmachinelearningalgorithms AT dieutienbui mappingwinderosionhazardwithregressionbasedmachinelearningalgorithms AT adrianlcollins mappingwinderosionhazardwithregressionbasedmachinelearningalgorithms
_version_	1718393863320633344

Mapping wind erosion hazard with regression-based machine learning algorithms

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