Based on this body of knowledge, RF should be, in theory, highly applicable to downscaling and able to rectify multivariable and nonlinear issues. By permuting the variables randomly, each variable can be compared to the prediction results and evaluated for its importance. The second is the inbuilt variable importance evaluation. The first is the ability to handle large datasets with correlated conditional variables, because it includes precision in the prediction, is nonparametric, and is robust in the presence of outliers, noise, and overfitting. There are two important advantages of RF models. RF models have been well applied in various fields, such as risk analysis, ground water studies, remote sensing analysis, and flood hazard assessment and especially show advantages in land cover classification. The random forest (RF) model is an ensemble machine learning technique based on a combination of classification or regression methods and statistical learning theory. Therefore, a precise statistical downscaling method with an inbuilt predictor selection mechanism will be helpful for researchers studying climate change impact. However, some limitations are found during the application, such as the limited ability of traditional correlation analysis for interpreting nonstationary and nonlinear relationships. Interactive model fitting approaches are also used in predictor selection. Informative predictors can be identified using statistical measures, such as the Pearson, Spearmen, and Kendall correlation analysis, CCA, maximum covariance analysis (MCA), partial correlation (PAR), and principal component analysis (PCA). Suitable predictors should be informative, and the relationship between the predictors and predictands should be stationary. The predictor selection is critical for developing a statistical downscaling model. These comparison studies indicate that none of the aforementioned methods can assure an accurate estimate of temperature under different situations. Duhan and Pandey compared MLR, ANN, and the least square support vector machine (LS-SVM) models to downscale the temperature of the Tons River basin in India and demonstrated that LS-SVM models perform better than ANN and MLR models. demonstrated that MLR and CCA are superior to ANN in the simulation of minimum and maximum temperatures over Greece. Schoof and Pryor demonstrated that ANN models give better estimates than multiple linear regression (MLR) models for daily temperature downscaling at Indianapolis. Some comparison studies have been made in the past. Various models have been developed and applied in the downscaling of temperature, like linear regression, canonical correlation analysis (CCA), artificial neural networks (ANN), support vector machines (SVM), and so forth. Statistical downscaling techniques can be divided into three categories: weather typing, weather generators, and regression-based methods. Because of the complexity in modeling and computing dynamic downscaling, statistical downscaling techniques have been used widely in climate change studies due to their simplicity and ease of implementation. These methods are widely divided into dynamic (physical) and statistical (empirical) downscaling. Various techniques have been developed to downscale GCM outputs to finer scales. However, there is a general mismatch between the spatial and temporal resolution of the GCM output and regional scale climate change impact studies. Global climate models (GCMs) are considered the most credible tools for the projection of future global climate change.
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The results indicate that the RF is a feasible tool for the statistical downscaling of temperature. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models.
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Matlab 2017 random binary series#
Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation.