Is a multiple regression method aiming to identify the best fit model predictors. This method tries to optimize the choice of the explanatory variables that predict the dependent variable. Stepwise regression automatically consists of iterative model construction.
Published in Chapter:
Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm
Ismail Elhassnaoui (Mohammadia School of Engineers, Rabat, Morocco), Zineb Moumen (University Politecnica de Catalunya, Spain), Hicham Ezzine (Mohammadia School of Engineers, Rabat, Morocco), Marwane Bel-lahcen (Advanced Digital Entreprise Modeling and Information Retrieval, Morocco), Ahmed Bouziane (Mohammadia School of Engineers, Rabat, Morocco), Driss Ouazar (Mohammadia School of Engineers, Rabat, Morocco), and Moulay Driss Hasnaoui (Ministry of Equipment, Transport, Logistics, and Water, Rabat, Morocco)
Copyright: © 2021
|Pages: 34
DOI: 10.4018/978-1-7998-3343-7.ch001
Abstract
In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.