2018
DOI: 10.1002/joc.5508
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Cautionary note on the use of genetic programming in statistical downscaling

Abstract: The selection of inputs (predictors) to downscaling models is an important task in any statistical downscaling exercise. The selection of an appropriate set of predictors to a downscaling model enhances its generalization skills as such set of predictors can reliably explain the catchment‐scale hydroclimatic variable (predictand). Among the predictor selection procedures seen in the literature, the use of genetic programming (GP) can be regarded as a unique approach as it not only selects a set of predictors i… Show more

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Cited by 28 publications
(12 citation statements)
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“…In this study, NMSE was computed by dividing the mean square error with the standard deviation of observations of the predictand. Unlike, mean square error and root mean square error, NMSE is less sensitive to the order of the magnitude of data of the predictands, hence it can be used to compare the performance of models pertaining to different climate regimes (Sachindra et al 2018b).…”
Section: Pmpgp-based Model Developmentmentioning
confidence: 99%
See 1 more Smart Citation
“…In this study, NMSE was computed by dividing the mean square error with the standard deviation of observations of the predictand. Unlike, mean square error and root mean square error, NMSE is less sensitive to the order of the magnitude of data of the predictands, hence it can be used to compare the performance of models pertaining to different climate regimes (Sachindra et al 2018b).…”
Section: Pmpgp-based Model Developmentmentioning
confidence: 99%
“…This indicated that data redundancies are not in direct connection with the simulation of unphysically large values of predictands. Sachindra et al (2018b) stated that quite often machine learning techniques simulate outliers and some of these outliers can be unphysically large. In the GP and PMPGP algorithms, the mathematical function set contained e x (exponential) and ln (natural logarithm) which are useful in capturing extremes in the time series of a predictand.…”
Section: Comparison Of Performance Of Gp and Pmpgp-based Downscaling mentioning
confidence: 99%
“…These studies require projected hydrometeorological time series and use the output of global climate models as primary information. However, the direct application of this output for impact modelling is hindered by climate model biases (Kotlarski et al, 2014;Tabari et al, 2016), the mismatch in temporal and spatial resolutions between the climate model output, and the time series required for impact modelling (Cristiano et al, 2018;Salvadore et al, 2015). Therefore, statistical downscaling or dynamical downscaling is applied.…”
Section: Introductionmentioning
confidence: 99%
“…Downscaling methods are the universal solutions to address the problems of a low resolution. Conventional methods include linear regression, polynomial regression, and other machine learning methods [22][23][24][25][26]. Previous studies have established the relationships between precipitation, the normalized difference vegetation index (NDVI), and topography information by linear regression or polynomial regression to achieve downscaling precipitation [18,[25][26][27][28].…”
Section: Introductionmentioning
confidence: 99%