logo
ResearchBunny Logo
Mapping wind erosion hazard with regression-based machine learning algorithms

Earth Sciences

Mapping wind erosion hazard with regression-based machine learning algorithms

H. Gholami, A. Mohammadifar, et al.

Explore the impact of wind erosion hazards in Isfahan province, Iran, with cutting-edge regression-based machine learning methods employed by Hamid Gholami, Aliakbar Mohammadifar, Dieu Tien Bui, and Adrian L. Collins. Discover how DEM, precipitation, and vegetation play pivotal roles in shaping these environmental challenges.

00:00
00:00
Playback language: English
Abstract
This research maps land susceptibility to wind erosion hazard in Isfahan province, Iran, using sixteen advanced regression-based machine learning methods. Thirteen factors influencing wind erosion were analyzed for multicollinearity, and model performance was assessed using RMSE, MAE, MBE, and Taylor diagrams. Five models (MMLPNN, SGAM, Cforest, BGAM, and SGB) showed high prediction accuracy, with DEM, precipitation, and vegetation (NDVI) identified as the most critical factors.
Publisher
Scientific Reports
Published On
Nov 24, 2020
Authors
Hamid Gholami, Aliakbar Mohammadifar, Dieu Tien Bui, Adrian L. Collins
Tags
wind erosion
machine learning
hazard mapping
regression analysis
Isfahan province
environmental factors
predictive modeling

Related Publications

Explore these studies to deepen your understanding of the subject.

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
Medicine and Health

Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms

P. Amiri, M. Montazeri, et al.

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application
Medicine and Health

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application

M. Karabacak, P. Jagtiani, et al.

Stylistic and linguistic variations in compliments: an empirical analysis of children's gender schema development with machine learning algorithms
Linguistics and Languages

Stylistic and linguistic variations in compliments: an empirical analysis of children's gender schema development with machine learning algorithms

X. Liao and Y. Zhang

Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach
Chemistry

Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach

Z. Song, H. Zhou, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny