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), 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.

Climate Change and Weather Extremes in the Eastern Mediterranean and Middle East

“…The majority of such studies emphasized on trace metals, organics and micro‐organisms in total suspended particulates or PM 10 and PM 2.5 samples (Al‐Khashman, 2013; Gat et al., 2017; Heidari‐Farsani et al., 2013; Lang‐Yona et al., 2020; Mazar et al., 2016; Rashki, Eriksson, et al., 2013; Shahsavani et al., 2012). Declining precipitation and evapotranspiration increases may lead to a decrease in vegetation, expanding the bare surface, which becomes more susceptible to wind erosion, causing an increase in dust activity, and the frequency and intensity of dust storms (Gholami et al., 2020; Middleton, 2019; Rashki et al., 2021; Shaheen et al., 2020). In addition, projected synoptic variations during the twenty‐first century that include increases in the frequency of Red‐Sea trough days were associated with dust storms in the Middle‐East (Elhacham & Alpert, 2020).…”

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

“…Consequently, complex techniques have been developed by combining two or more statistical techniques; for instance, merging MEM and GWR [67] or incorporating MEM into GAM [68]. Nowadays, Machine Learning (ML) techniques such as deep neural network (DNN), support vector regression (SVR) and random forest (RF) enable to capture the complex relationships between parameters, exhibiting greater performance in estimating PM 2.5 [69,70] and are increasingly used in air quality studies [71][72][73][74]. Furthermore, some studies have incorporated meteorological factors with land use variables to predict the spatial and temporal variation of aeolian erosion and PM [75][76][77][78][79].…”

Activities of Microplastics (MPs) in Agricultural Soil: A Review of MPs Pollution from the Perspective of Agricultural Ecosystems

“…Microplastics can change the basic properties of soil (Figure ). Bulk density is a commonly reported soil physical property, which has been proven to be closely related to soil erosion risk . Polyester, poly­(acrylic acid), and polyethylene can increase the soil water holding capacity and decreased bulk density. , de Souza Machado et al (2018) reported that fiber polyester reduced soil bulk density to a greater extent than fragments and beads .…”