A SVR–ANN combined model based on ensemble EMD for rainfall prediction

Xiang, Yu; Gou, Ling; He, Lihua; Xia, Shoulu; Wang, Wenyong

doi:10.1016/j.asoc.2018.09.018

Cited by 132 publications

(48 citation statements)

References 39 publications

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“…ANN model has been described by several researches in detail [40][41][42]. ANN is a nonlinear model which is composed of a large neuron collection.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

RSM and ANN modeling of the energy absorption behavior of steel thin-walled columns: a multi-objective optimization using the genetic algorithm

Dadrasi

Albooyeh

Fooladpanjeh

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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In many industries, it is necessary to use structures that exhibit a proper stability against the design loads and depreciate the energy in a controlled manner. In this study, the energy absorption characteristics of thin-walled structures with rectangular cross sections are investigated under the quasi-static loading. The section of structures has a different aspect ratio, and in all of them, an elliptical cutout with a different diameter ratio exists on the larger side. In all instances, the area of the cross section and cutout is constant. Hence, an experimental design with two design parameters consisting of the shell aspect ratio and the diameter ratio of the cutout was conducted by applying the central composite design method. Energy absorption parameters were modeled using the artificial neural network and the response surface method. A systematic crashworthiness study was carried out with a multi-objective optimization design using the genetic algorithm. The results showed that the optimal amount of the specific energy absorption was 14.48 kJ/kg and the optimal amount of the peak crushing load was 37.77 kN which was obtained in the aspect ratio of 1 and the diameter ratio of 0.7. The validity of the results was confirmed by empirical experiments.

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“…ANN model has been described by several researches in detail [40][41][42]. ANN is a nonlinear model which is composed of a large neuron collection.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

RSM and ANN modeling of the energy absorption behavior of steel thin-walled columns: a multi-objective optimization using the genetic algorithm

Dadrasi

Albooyeh

Fooladpanjeh

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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show abstract

“…However, many real-world applications like wind power prediction [3] are too complex to be modeled by the above single global model. In recent years, support vector regression (SVR) [4] has been applied in nonlinear regression forecasting. It was proposed by the AT&T BELL Laboratories for classification and was then extended for the purpose of regression.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Nonlinear Regression with Missing Data Using Quasilinear Kernel

Zhu

Tian

Ren

et al. 2020

IEEJ Transactions Elec Engng

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In both the research and engineering fields, missing data is a serious problem that cannot be overlooked. Therefore, available datasets with missing data are a challenge to be modeled by conventional global prediction models. In this paper, we propose a hybrid model consisting of an autoencoder and a gated linear network for solving the regression problem under missing value scenario. A sophisticated modeling and identifying algorithm is developed. First, an extended affinity propagation (AP) clustering algorithm is applied to obtain a self-organized competitive net dividing the datasets into several clusters. Second, a multiple imputation tool with top p% winner-take-all denoising autoencoders (DAE) is introduced to realize better predictions of missing values, in which rough estimates of missing values by using the mean imputation and similarity method within the clusters are used as teacher signals of DAE. Finally, a gated linear network is designed to construct a piecewise linear regression model with interpolations in the exact same way as a support vector regression with a quasilinear kernel composed using the cluster information obtained in the AP clustering step. Based on the experiments of five datasets, our proposed method demonstrates its effectiveness and robustness compared with other traditional kernels and state-of-the-art methods, even on datasets with a large percentage of missing values.

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“…MLMs coupled with decomposition techniques are employed, to enhance the performance of standalone models, and for more accurate prediction [25,26]. e decomposition techniques have been effectively applied to decompose the streamflow time series and to improve the performance of MLMs [27].…”

Section: Introductionmentioning

confidence: 99%

A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

Sibtain

Saleem

2020

Advances in Meteorology

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The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction.

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A SVR–ANN combined model based on ensemble EMD for rainfall prediction

Cited by 132 publications

References 39 publications

RSM and ANN modeling of the energy absorption behavior of steel thin-walled columns: a multi-objective optimization using the genetic algorithm

RSM and ANN modeling of the energy absorption behavior of steel thin-walled columns: a multi-objective optimization using the genetic algorithm

A Hybrid Model for Nonlinear Regression with Missing Data Using Quasilinear Kernel

A Multivariate and Multistage Medium- and Long-Term Streamflow Prediction Based on an Ensemble of Signal Decomposition Techniques with a Deep Learning Network

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