A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Abstract

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

Research on Power Grid Investment Risk of Set-Analysis Method in New Electricity Reform

“…On the other hand, the degree of contact is less than 0, indicating that the investment risk will become smaller. According to this trend, the investment risk will gradually become smaller, and such investment plans are also easily accepted [11] .The grid enterprise management personnel set the threshold according to their actual situation and the degree of resistance to the risk, so as to measure the anti-risk ability.…”

“…The net present value method cannot accurately estimate the investment risk of the project. The literature [11] uses the set pair analysis method to establish the grid investment risk control model in the market environment, and then quantify the risk of grid investment. Although the author uses the dynamic analysis method to study the uncertainty of investment risk, which applied in a novel way, but the applicable environment is inconsistent with the current power industry reform.…”

Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

“…In recent years, numerous experts and scholars have applied decomposition-reconstruction prediction models to predict targets that are obvious and have related fluctuations, such as wind speed and PV power generation. Two commonly used methods are wavelet analysis [25][26][27][28] and empirical mode decomposition (EMD) [29][30][31]. Both methods can decompose the original waveform and are able to improve the prediction accuracy; however, both have drawbacks.…”

Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis

“…It was initially proposed by Morlet et al [29,30] to analyze the seismic signals in geophysics and was popularized by Grossmann and Morlet [31] and Goupillaud et al [32]. Nowadays, WA becomes a new mathematical approach and is widely applied in geodesy and geophysics [33,34], remote sensing [35,36] and hydrology [37,38]. Unlike the Fourier transform (FT) [39] which maps a time series from time domain to frequency domain, the wavelet transform (WT) can simultaneously describe the time-frequency characteristics of a time series, which is one of the most important properties of the wavelets [40].…”