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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-4-2022
https://doi.org/10.5194/isprs-annals-V-4-2022-49-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-V-4-2022-49-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
18 May 2022
| 18 May 2022
STACKING-BASED UNCERTAINTY MODELLING OF STATISTICAL AND MACHINE LEARNING METHODS FOR RESIDENTIAL PROPERTY VALUATION
A. Jafari
Department of GIS, School of Surveying and Geospatial Eng. College of Engineering, University of Tehran, Tehran, Iran
M. R. Delavar
Centre of Excellence in Geomatic Eng. in Disaster Management, School of Surveying and Geospatial Eng. College of Engineering, University of Tehran, Tehran, Iran
A. Stein
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, The Netherlands
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Manuscript not accepted for further review
Short summary
Short summary
Weather stations are often sparse and systematic under/overestimation of a global weather forecast system leads to bias. Most of the available bias correction methods do not consider higher order moments of a probability distribution and they use same distributions families to estimate both marginal and multivariate distributions. We propose three new copula-based bias correction methods, which describe the dependence structure between air temperature and covariates.
M. Ahmadlou, M. R. Delavar, H. Shafizadeh-Moghadam, and A. Tayyebi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 285–290, https://doi.org/10.5194/isprs-archives-XLI-B2-285-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-285-2016, 2016
A. A. Heidari and M. R. Delavar
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 299–304, https://doi.org/10.5194/isprs-archives-XLI-B2-299-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-299-2016, 2016
Rahul Raj, Nicholas Alexander Samuel Hamm, Christiaan van der Tol, and Alfred Stein
Biogeosciences, 13, 1409–1422, https://doi.org/10.5194/bg-13-1409-2016, https://doi.org/10.5194/bg-13-1409-2016, 2016
Short summary
Short summary
We present a Bayesian estimation of uncertainty in half-hourly GPP partitioned from flux tower measurements of NEE. The results show that it is possible to do this at any desirable time step. This, in turn, can be used to quantify the propagated uncertainty when validating process-based simulators. We further show the importance of using non-informative priors compared to informative priors of the parameters of flux partitioning model as they speed up calculation without loss of precision.
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