Conjunction of wavelet-entropy and SOM clustering for multi-GCM statistical downscaling

Abstract

Important issues in statistical downscaling of general circulation models (GCMs) is to select dominant large-scale climate data (predictors). This study developed a predictor screening framework, which integrates wavelet-entropy (WE) and self-organizing map (SOM) to downscale station rainfall. WEs were computed as the representatives of predictors and fed into the SOM to cluster the predictors. SOM-based clustering of predictors according toWEs could lead to physically meaningful selection of the dominant predictors. Then, artificial neural network (ANN) as the statistical downscaling method was developed. To assess the advantages of different GCMs, multi-GCM ensemble approach was used by Can-ESM2, BNU-ESM, and INM-CM4 GCMs. Moreover, NCEP reanalysis data were used to calibrate downscaling model as well for comparison purposes. The calibration, validation, and projection of the proposed model were performed during , respectively. The proposed data screening model could reduce the dimensionality of data and select appropriate predictors for generalizing future rainfall. Results showed better performance of ANN than multiple linear regression (MLR) model. The projection results yielded 29% and 21% decrease of rainfall at the study area for 2017-2050 under RCPs 4.5 and 8.5, respectively.

Optimization of support vector machine for transformer core and winding looseness fault diagnosis based on improved grey wolf algorithm

“…Energy entropy is a kind of information entropy that represents the distribution of energy in a signal and has the advantages of simple calculation and strong noise resistance [8]. In transformer fault diagnosis, the looseness of the transformer core and winding is often accompanied by specific fault frequencies, due to CEEMD decomposing transformer vibration signals into a series of sparse and bandwidth-limited IMF components.…”

“…Therefore, in this paper, the energy entropy of each IMF constitutes a set of characteristic quantities to represent the state of the transformer windings. The process of calculating energy entropy is described in detail in [8], which will not be elaborated in this article.…”

Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method

“…The historical and future data for the period of 1951-2060 were retrieved from https://esgfnode.llnl.gov/. Climate models including BNU-ESM, Can-ESM5, and INM-CM5, which respectively have been developed by research centers in China, Canada, and Russia, were used in this study (Table 1), owing to the preceding research works proving that these three climate models appear to be more effective for investigating the climate of the region (Baghanam et al 2018;Nourani et al 2019aNourani et al , 2019b. Since, in this study, two Earth system models (ESMs) and one GCM were used, henceforth the term climate models (CMs) will be used to refer to both models.…”

“…The datasets were divided into two parts: the first 75% of the dominant inputs and observed data which is equal to the period 1951-1991 were used for calibration, and the 25% of them which is equivalent to the period 1992-2005 were applied to validation step. Prior studies have indicated the effectiveness of such data partitioning for the purpose of investigating the hydro-climatological processes (Baghanam et al 2018;Nourani et al 2019aNourani et al , 2019b.…”

“…In the field of statistical downscaling, Rana & Moradkhani (2016) employed continuous wavelet transform (CWT) in the projection step to analyze the possible variability in future precipitation and temperature. More recently, Baghanam et al (2018) and Nourani et al (2018aNourani et al ( , 2018b utilized wavelet entropy (WE) to downscale the precipitation. Based on the preceding studies, CWT and the discrete wavelet transform (DWT) have been applied for feature extraction and denoising of a single signal, however, the application of the two methods has not been investigated for predictor screening purposes.…”