R
Roozbeh Valavi
Researcher at University of Melbourne
Publications - 25
Citations - 1000
Roozbeh Valavi is an academic researcher from University of Melbourne. The author has contributed to research in topics: Computer science & Biology. The author has an hindex of 9, co-authored 20 publications receiving 413 citations. Previous affiliations of Roozbeh Valavi include Shahid Beheshti University.
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blockCV: An r package for generating spatially or environmentally separated folds for k‐fold cross‐validation of species distribution models
TL;DR: The r package blockCV as mentioned in this paper is a toolbox for cross-validation of species distribution modeling, which can be used for any spatial modelling. But it is not suitable for the analysis of structured data, as it may lead to underestimation of prediction error and may result in inappropriate model selection.
Journal ArticleDOI
Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.
TL;DR: To reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment.
Posted ContentDOI
blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models
TL;DR: The R package blockCV is presented, a new toolbox for cross-validation of species distribution modelling that includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data.
Journal ArticleDOI
Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space
Ruhollah Taghizadeh-Mehrjardi,Karsten Schmidt,Alireza Amirian-Chakan,Tobias Rentschler,Mojtaba Zeraatpisheh,Fereydoon Sarmadian,Roozbeh Valavi,Naser Davatgar,Thorsten Behrens,Thomas Scholten +9 more
TL;DR: The stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions and indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy.