Volume 77 (2024)
Volume 76 (2023)
Volume 75 (2022)
Volume 74 (2021)
Volume 73 (2020)
Volume 72 (2019)
Volume 71 (2018)
Volume 70 (2017)
Volume 69 (2016)
Volume 68 (2015)
Volume 67 (2014)
Volume 66 (2013)
Volume 65 (2012)
Volume 63 (2010)
Volume 62 (2009)
Investigating the effectiveness of resampling algorithms in improving the classification of unbalanced data in digital soil mapping

Fatemeh Ebrahimi Meymand; Hasan Ramezanpour; Nafiseh Yaghmaeian; Kamran Eftekhari

Volume 76, Issue 2 , August 2023, , Pages 159-176

https://doi.org/10.22059/jrwm.2023.354333.1692

Abstract
  In recent years, the use of digital soil mapping (DSM) based on machine learning algorithms with the aim of preparing soil maps has become widespread with the basis of soil class prediction with the help of modeling the relationships between them and environmental variables. One of this method's challenges ...  Read More

Comparison of machine learning models to prioritize susceptible areas to dust production

Serveh Darvand; Hassan Khosravi; Hamidreza Keshtkar; Gholamreza Zehtabian; Omid Rahmati

Volume 74, Issue 1 , June 2021, , Pages 53-68

https://doi.org/10.22059/jrwm.2021.321033.1580

Abstract
  The purpose of this study was to compare machine learning models including Support Vector Machine, Classification and Regression Tree, Random Forest, and Multivariate Discriminate Analysis to prioritize susceptible areas to dust production. To determine the dust days, hourly meteorological data of Alborz ...  Read More

Spatial prediction of shallow landslides using statistical and machine learning models (case study: Sarkhoon watershed)

Zahra Barati; Ebrahim Omidvar; Ataollah Shirzadi

Volume 71, Issue 4 , March 2019, , Pages 869-884

https://doi.org/10.22059/jrwm.2018.268247.1314

Abstract
  Landslide susceptibility mapping is considered as the first important step in landslide risk assessment. The main purpose of this study is to compare the performance of a machine learning algorithm (a logistic model tree), and a statistical model (a logistic regression), for landslide susceptibility ...  Read More