scholarly journals Wavelet-genetic programming conjunction model for flood forecasting in rivers

2018 ◽  
Vol 49 (6) ◽  
pp. 1880-1889 ◽  
Author(s):  
Mani Kumar ◽  
Rajeev Ranjan Sahay
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Genetic Programming ◽  
Flow Time ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Performance Indices ◽  
Flow Rates ◽  
The World ◽  
Daily Flows

Abstract In this study we have developed a conjunction model, WGP, of discrete wavelet transform (DWT) and genetic programming (GP) for forecasting river floods when the only data available are the historical daily flows. DWT is used for denoising and smoothening the observed flow time series on which GP is implemented to get the next-day flood. The new model is compared with autoregressive (AR) and stand-alone GP models. All models are calibrated and tested on the Kosi River which is one of the most devastating rivers of the world with high and spiky monsoon flows, modeling of which poses a great challenge. With different inputs, 12 models, four in each class of WGP, GP and AR, are devised. The best performing WGP model, WGP4, with four previous daily flow rates as input, forecasts the Kosi floods with an accuracy of 87.9%, root mean square error of 123.9 m3/s and Nash–Sutcliffe coefficient of 0.993, the best performance indices among all the developed models. The extreme floods are also better simulated by the WGP models than by AR and GP models.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

scholarly journals Financial Time Series Image Algorithm Based on Wavelet Analysis and Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wuwei Liu ◽  
Jingdong Yan
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Mean Square Error ◽  
Financial Time Series ◽  
Prediction Method ◽  
Mean Square ◽  
Rbf Network ◽  
Network Prediction ◽  
The Mean ◽  
Hidden Layer

In recent years, people are more and more interested in time series modeling and its application in prediction. This paper mainly discusses a financial time series image algorithm based on wavelet analysis and data fusion. In this research, we conducted an in-depth study on the scale decomposition sequence and wavelet transform sequence in different scale domains of wavelet transform according to the scale change rule based on wavelet transform. We use wavelet neural network with different input neurons and hidden neurons to predict, respectively. Finally, the prediction results are integrated into the final prediction results based on the original time series by using wavelet reconstruction technology. Using RBF algorithm in neural network and SPSS Clementine, the wavelet transform sequences on five scales are modeled. Each network model has three layers: one input layer, one hidden layer, and one output layer, and each output layer has only one output element. In order to compare the prediction effect of the model proposed in this study, the ordinary RBF network is used to model and predict the log yield itself. When the input sample is 5, the minimum mean square error is obtained when the hidden layer is 6, and the mean square error is 1.6349. The mean square error of the training phase is 0.0209, and the validation error is 1.6141. The results show that the prediction results of the wavelet prediction method combined with the RBF network prediction method are better than those of wavelet prediction or RBF network prediction.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS

2004 ◽  
Vol 02 (04) ◽  
pp. 567-587 ◽  
Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multivariate Time Series ◽  
Discrete Wavelet ◽  
Pass Filter ◽  
Wavelet Filters ◽  
The Hilbert Transform ◽  
High Pass

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.

Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds

2020 ◽  
Vol 234 (17) ◽  
pp. 3521-3540
Author(s):  
Hoi Yin Sim ◽  
Rahizar Ramli ◽  
Ahmad Saifizul
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Acoustic Emission Signal ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Crest Factor ◽  
Emission Signal ◽  
Best Parameter ◽  
All Speeds

Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.

scholarly journals MODELLING OF PARTIAL DISCHARGE ANALYSIS SYSTEM USING WAVELET TRANSFORM DENOISING TECHNIQUE IN LABVIEW ENVIRONMENT

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
A. Nazifah Abdullah ◽  
S. H. K. Hamadi ◽  
M. Isa ◽  
B. Ismail ◽  
A. N. Nanyan ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Virtual Instrument ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Mother Wavelet ◽  
Signal To Noise ◽  
Labview Software ◽  
Analysis System

Partial discharge (PD) measurement is an essential to detect and diagnose the existence of the PD. However, this measurement has faced noise disturbance in industrial environments. Thus, PD analysis system using discrete wavelet transform (DWT) denoising technique via Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software is proposed to distinguish noise from the measured PD signal. In this work, the performance of denoising process is analyzed based on calculated mean square error (MSE) and signal to noise ratio (SNR). The result is manipulated based on Haar, Daubechies, Coiflets, Symlets and Biorthogonal type of mother wavelet with different decomposition levels. From the SNR results, all types of the mother wavelet are suitable to be used in denoising technique since the value of SNR is in large positive value. Therefore, further studies were conducted and found out that db14, coif3, sym5 and bior5.5 wavelets with least MSE value are considered good to be used in the denoising technique. However, bior5.5 wavelet is proposed as the most optimum mother wavelet due to consistency of producing minimum value of MSE and followed by db14.

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