Forecasting salinity time series using RF and ELM approaches coupled with decomposition techniques

Hu, Jiayu; Liu, Bingjun; Sihan, Peng

doi:10.1007/s00477-019-01691-1

Cited by 41 publications

(18 citation statements)

References 74 publications

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“…The conventional Fourier transform was extended as wavelet transform that provides a multiresolution analysis of signals. Wavelet packets (WP) are the generalization of wavelet transform capable of providing better frequency localization of signals [36]. The WP provides extensive decomposition over classic wavelet transform.…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

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Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

2021

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This study presents developing procedures and verification of a new hybrid model, namely wavelet packet-genetic programming (WPGP) for short-term meteorological drought forecast. To this end, the multi-temporal standardized precipitation evapotranspiration index (SPEI) has been used as the drought quantifying parameter at two meteorological stations at Ankara province, Turkey. The new WPGP model comprises two main steps. In the first step, the wavelet packet, which is a generalization of the well-known wavelet transform, is used to decompose the SPEI series into deterministic and stochastic sub-signals. Then, classic genetic programming (GP) is applied to formulate the deterministic sub-signal considering its effective lags. To characterize the stochastic component, different theoretical probability distribution functions were assessed, and the best one was selected to integrate with the GPevolved function. The efficiency of the new model was cross-validated with the first order autoregressive (AR1), GP, and random forest (RF) models developed as the benchmarks in the present study. The results showed that the WPGP is a robust model, superior to AR1 and RF, and significantly increases the predictive accuracy of the standalone GP model.

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Section: Wavelet Packet Transformmentioning

confidence: 99%

“…Indeed, WPs are ways of mixing and matching wavelet filter banks in tree-like structures to create arbitrary time-frequency tiling. Recently WP has been compared with discrete wavelet transform [36].…”

Section: Wavelet Packet Transformmentioning

confidence: 99%

Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

2021

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“…This wavelet transformed series allow the capturing of different intrinsic details relevant for different time scales, which could be of importance for predicting scale dependent properties and which would hardly be identified otherwise. The advantages of decomposition methods for forecasting models have been shown in the work of Hu [19].…”

Section: Datamentioning

confidence: 99%

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

2019

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Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. Several different Machine Learning (ML) methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. The methods analysed include Multivariate Adaptive Regression Splines (MARS) and various Quantile Regression (QR) models like Quantile Random Forest (QRF) and Gradient Boosting Machines (GBM). Additionally, a Nonhomogeneous Gaussian Regression (NGR) approach has been tested for combining and calibrating monthly ML based forecasts driven by ensemble weather forecasts. The novelty and main focus of this study is the comparison of the capability of ML methods for producing reliable predictive uncertainties and the application of monthly weather forecasts. Different skill scores have been used to verify the predictions and their uncertainties and first results for combining the ML methods applying the NGR approach and coupling the predictions with monthly ensemble weather forecasts are shown for the southern Switzerland (Canton of Ticino). These results highlight the possibilities of improvements using ML methods and the importance of optimally combining different ML methods for achieving more accurate estimates of future energy consumptions and productions with sharper prediction uncertainty estimates (i.e., narrower prediction intervals).

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“…In this regard, hybrid forecast framework coupling data pre-/post-processing methods (e.g. singular spectrum analysis, empirical mode decomposition and principal component analysis) with data-driven models are usually used to overcome the shortcomings of a single model (Hu et al 2019;Meng et al 2019;Tayfur et al 2013; Unnikrishnan and Jothiprakash, 2018). Recently, wavelet-based data-driven models have received much attention owing to their advantage of being able to extract dynamic and multi-scale features from nonstationary hydrological time series.…”

Section: Introductionmentioning

confidence: 99%

Streamflow Prediction Improvement using Deterministic and Stochastic Wavelet-based Methods Coupled with Extreme Learning Machine

Hu¹,

Liu²

2021

Preprint

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Accurate and reliable streamflow forecasting is important in hydrology and water resources planning and management. In the present work, wavelet-based direct (DF) and multi-component (MF) forecast methods performed by the à trous algorithm (AT) are proposed for both deterministic and stochastic monthly streamflow prediction improvement. They are developed in the case of the one-month lead streamflow prediction of the East River basin in China, and then compared with the benchmarks that are implemented without wavelet transform so as to evaluate the effectiveness for forecasting accuracy improvement. An existing blueprint that is flexible and practical to incorporate various sources of forecast uncertainty is extended to generate the stochastic probability prediction of streamflow. Partial mutual information is adopted for predictors selection, and six kinds of Extreme learning machine (i.e. one linear ELM and five common nonlinear kinds) are separately used as the learning algorithms coupled with the wavelet-based forecast methods to conduct a comprehensive performance evaluation. The comparison results indicate that both DF and MF can effectively increase the point prediction accuracy of monthly streamflow under deterministic and stochastic forecasting conditions, while MF performs better than DF. For stochastic prediction, it is much more reasonable to consider both parameter and model error uncertainties than just to consider only parameter uncertainty, and with the reasonable setting MF method can significantly improve the probabilistic interval prediction by greatly improving the forecast sharpness. It can be concluded that the approach using AT wavelet-based DF or MF could provide a feasible way for streamflow prediction improvement.

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Forecasting salinity time series using RF and ELM approaches coupled with decomposition techniques

Cited by 41 publications

References 74 publications

Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

Streamflow Prediction Improvement using Deterministic and Stochastic Wavelet-based Methods Coupled with Extreme Learning Machine

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