BALZARINI MONICA GRACIELA
Artículos
Título:
A spatially based quantile regression forest model for mapping rural land values
Autor/es:
CÓRDOBA, MARIANO; CARRANZA, JUAN PABLO; PIUMETTO, MARIO; MONZANI, FEDERICO; BALZARINI, MÓNICA
Revista:
JOURNAL OF ENVIRONMENTAL MANAGEMENT
Editorial:
ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD
Referencias:
Año: 2021 vol. 289
ISSN:
0301-4797
Resumen:
ural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting economic values. New machine learning methods, like tree-based regression models, have been proposed as an alternative to linear regression to predict economic values from ancillary variables, since these algorithms are able to handle non-normality and non-linearity in the data. However, regression trees are commonly estimated assuming independent rather than spatially correlated data. This study aims to build a tree-based regression model that will help to tackle methodological problems related to the determination of prices of rural lands. The Quantile Regression Forest (QRF) algorithm was used to provide a regression model to predict and assess the uncertainty associated with model-derived predictions. However, the classical QRF ignores t