scholarly journals A Comparison of the Discrete Cosine and Wavelet Transforms for Hydrologic Model Input Data Reduction

2017 ◽  
Author(s):  
Ashley Wright ◽  
Jeffrey P. Walker ◽  
David E. Robertson ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Parameter Estimation ◽  
Data Reduction ◽  
Wavelet Transforms ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Discrete Wavelet ◽  
Rainfall Time Series ◽  
Data Set ◽  
Model Input

Abstract. The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Model input data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are used to reduce the dimensionality of rainfall time series observations from the 438 catchments in the MOdel Parameter Estimation eXperiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the measured hyetographs using standard simulation performance summary metrics and descriptive statistics as well as peak discharge errors. The results convincingly demonstrate that the DWT is superior to the DCT and best preserves and characterizes the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

scholarly journals A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction

2017 ◽  
Vol 21 (7) ◽  
pp. 3827-3838 ◽  
Author(s):  
Ashley Wright ◽  
Jeffrey P. Walker ◽  
David E. Robertson ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Parameter Estimation ◽  
Data Reduction ◽  
Wavelet Transforms ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Discrete Wavelet ◽  
Rainfall Time Series ◽  
Data Set ◽  
Model Input

Abstract. The treatment of input data uncertainty in hydrologic models is of crucial importance in the analysis, diagnosis and detection of model structural errors. Data reduction techniques decrease the dimensionality of input data, thus allowing modern parameter estimation algorithms to more efficiently estimate errors associated with input uncertainty and model structure. The discrete cosine transform (DCT) and discrete wavelet transform (DWT) are used to reduce the dimensionality of observed rainfall time series for the 438 catchments in the Model Parameter Estimation Experiment (MOPEX) data set. The rainfall time signals are then reconstructed and compared to the observed hyetographs using standard simulation performance summary metrics and descriptive statistics. The results convincingly demonstrate that the DWT is superior to the DCT in preserving and characterizing the observed rainfall data records. It is recommended that the DWT be used for model input data reduction in hydrology in preference over the DCT.

scholarly journals Identification of Hydrologic Models, Optimized Parameters, and Rainfall Inputs Consistent with In Situ Streamflow and Rainfall and Remotely Sensed Soil Moisture

2018 ◽  
Vol 19 (8) ◽  
pp. 1305-1320 ◽  
Author(s):  
Ashley J. Wright ◽  
Jeffrey P. Walker ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Input Data ◽  
Remotely Sensed ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Model Input ◽  
Rainfall Estimates

Abstract An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall–runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall–runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.

Study of Preprocessing Methods for the Determination of Crystalline Phases in Binary Mixtures of Drug Substances by X-ray Powder Diffraction and Multivariate Calibration

2000 ◽  
Vol 54 (8) ◽  
pp. 1222-1230 ◽  
Author(s):  
Tom Artursson ◽  
Anders Hagman ◽  
Seth Björk ◽  
Johan Trygg ◽  
Svante Wold ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Powder Diffraction ◽  
Wavelet Transforms ◽  
Multivariate Calibration ◽  
Discrete Wavelet ◽  
Data Set ◽  
X Ray ◽  
Drug Substances ◽  
Orthogonal Signal Correction

In this paper, various preprocessing methods were tested on data generated by X-ray powder diffraction (XRPD) in order to enhance the partial least-squares (PLS) regression modeling performance. The preprocessing methods examined were 22 different discrete wavelet transforms, Fourier transform, Savitzky–Golay, orthogonal signal correction (OSC), and combinations of wavelet transform and OSC, and Fourier transform and OSC. Root mean square error of prediction (RMSEP) of an independent test set was used to measure the performance of the various preprocessing methods. The best PLS model was obtained with a wavelet transform (Symmlet 8), which at the same time compressed the data set by a factor of 9.5. With the use of wavelet and X-ray powder diffraction, concentrations of less than 10% of one crystal from could be detected in a binary mixture. The linear range was found to be in the range 10–70% of the crystalline form of phenacetin, although semiquantitative work could be carried out down to a level of approximately 2%. Furthermore, the wavelet-pretreated models were able to handle admixtures and deliberately added noise.

scholarly journals A NEW LOOK AT BANKRUPTCY MODELS

2021 ◽  
Vol 24 (3) ◽  
pp. 167-185
Author(s):  
Michal Kuběnka ◽  
Jan Čapek ◽  
František Sejkora
Keyword(s):  
Error Rate ◽  
Input Data ◽  
Bankruptcy Prediction ◽  
Data Uncertainty ◽  
Data Set ◽  
Accounting Systems ◽  
Limit Value ◽  
New Models ◽  
Score Model ◽  
The Individual

New models for bankruptcy prediction are constantly being formulated and tested against the current ones and current ones are tested to assess their current accuracy and to allow users to determine the reliability of the results when using the model. These models use accounting information as input data. Accounting systems, for example, US GAAP, or IFRS, contain rules that may be applied differently from one company to another without being breached. This leads to input data uncertainty. Likewise, uncertainties may arise due to errors in recording and transcribing input data or in translating the values of assets, equity or liabilities in foreign currencies. This research was focused on the effect of entry data uncertainty on models’ ability to accurately predict bankruptcy. The initial assumption was that raising the number of input values would increase the error rate probability in entry data, thus also heightening the uncertainty of the results in the given bankruptcy prediction model. The data set of tested companies contained 1,220 non-bankrupt and 285 bankrupt Czech companies. The tested models – Z’ score, Model 1, and Ycz – were applied to this sample, and in all cases, the resulting accuracy was lower than the accuracy declared by their authors. A procedure was created for the inclusion of entry data uncertainty in the practical application of a model. This procedure consists of changing the limit value of the model that separates bankrupt and non-bankrupt companies to an interval that “absorbs” such uncertainties. The model cannot classify the companies in this interval. The research shows that the inclusion of uncertainties in entry data further reduces their accuracy. However, the reduction in accuracy between the individual models varies significantly from 2.2% to 39.4% for bankrupt companies, and from 3.5% to 91.8% for non-bankrupt companies, respectively. The analysis of the entry data uncertainty effect shows the need to create models with high precision and minimum of input values because the model error rate grows the higher their number. The findings of this research can be applied in the creation of new models for predicting bankruptcy not only in the Central Europe but globally.

scholarly journals Enhanced Epileptic Seizure diagnosis using EEG Signals with Support vector machine and Bagging Classifiers

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Rana Alrawashdeh ◽  
Mohammad Al-Fawa'reh ◽  
Wail Mardini
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Artificial Neural Network Ann ◽  
Manual Testing

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue to rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML can be used to classify EEG signals employing feature extraction techniques. This work focuses on automated detection for epilepsy seizures using ML techniques. Various algorithms are investigated, such as  Bagging, Decision Tree (DT), Adaboost, Support vector machine (SVM), K-nearest neighbors(KNN), Artificial neural network(ANN), Naïve Bayes, and Random Forest (RF) to distinguish injected signals from normal ones with high accuracy. In this work, 54 Discrete wavelet transforms (DWTs) are used for feature extraction, and the similarity distance is applied to identify the most powerful features. The features are then selected to form the features matrix. The matrix is subsequently used to train ML. The proposed approach is evaluated through different metrics such as F-measure, precision, accuracy, and Recall. The experimental results show that the SVM and Bagging classifiers in some data set combinations, outperforming all other classifiers

scholarly journals New prognostic neural method by discrete wavelet transforms for electromechanical flight controls affected by progressive faults

2018 ◽  
Vol 233 ◽  
pp. 00017
Author(s):  
Matteo D. L. Dalla Vedova ◽  
Nicola Lampariello ◽  
Paolo Maggiore
Keyword(s):  
Wavelet Transforms ◽  
Short Circuit ◽  
Critical Issue ◽  
Discrete Wavelet ◽  
Data Set ◽  
Electromechanical Actuators ◽  
Brushless Motor ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Flight Controls

Progressive failures affecting onboard electromechanical actuators (EMA), especially if related to primary flight commands, could be a critical issue for the aircraft reliability and, in the worst cases, could compromise its safety. In the last years strong interest is expected by the development of prognostic algorithms able to provide an early identification of the precursors of EMA progressive failures. In this work authors proposes a new prognostic method based on two artificial neural networks (ANN), a basic and an enhanced feedforward neural network, performing the fault detection and identification of two critical progressive faults often affecting the EMA brushless motor (i.e. turn-to-turn short circuit of a stator coil and rotor static eccentricity); in order to identify a suitable data set able to guarantee an affordable ANN classification, the said failures precursors are properly pre-processed by a Discrete Wavelet Transform, extracting several features used as input of the proposed prognostic algorithm.

scholarly journals Segmentation and Classification of Image Abnormalities in Retinal Fundus using Discrete Wavelet Transforms

2019 ◽  
Vol 8 (4) ◽  
pp. 11357-11360
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transforms ◽  
Region Of Interest ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Data Set ◽  
Retinal Fundus

Glaucoma disease diagnosis greatly based on the accurate retinal image segmentation and classification of images. Segmentation means to divide the images into a patchwork of regions, each of which is “homogeneous”, that is the “same” in some sense. Using discrete wavelet transform, the segmented images are classified by Support Vector Machine (SVM) classifiers to classify the Glaucoma images.The proposed Support Vector Machine classifier is used to extract the information rely on the Region of Interest (ROI) from original retinal fundus image. Thus the classification result are used to find the normal and abnormal image and also to compute the normal and abnormal accuracies.We observed an accuracy of around 93% using data set by SVM classifier.

scholarly journals Comparing impacts of parameter and spatial data uncertainty for a grid-based distributed watershed model

2016 ◽  
Vol 18 (6) ◽  
pp. 961-974 ◽  
Author(s):  
Younggu Her ◽  
Conrad Heatwole
Keyword(s):  
Land Cover ◽  
Parameter Uncertainty ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Model Output ◽  
Model Errors ◽  
Land Cover Data ◽  
The Impact

Parameter uncertainty in hydrologic modeling is commonly evaluated, but assessing the impact of spatial input data uncertainty in spatially descriptive ‘distributed’ models is not common. This study compares the significance of uncertainty in spatial input data and model parameters on the output uncertainty of a distributed hydrology and sediment transport model, HYdrology Simulation using Time-ARea method (HYSTAR). The Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm was used to quantify parameter uncertainty of the model. Errors in elevation and land cover layers were simulated using the Sequential Gaussian/Indicator Simulation (SGS/SIS) techniques and then incorporated into the model to evaluate their impact on the outputs relative to those of the parameter uncertainty. This study demonstrated that parameter uncertainty had a greater impact on model output than did errors in the spatial input data. In addition, errors in elevation data had a greater impact on model output than did errors in land cover data. Thus, for the HYSTAR distributed hydrologic model, accuracy and reliability can be improved more effectively by refining parameters rather than further improving the accuracy of spatial input data and by emphasizing the topographic data over the land cover data.

Sign in / Sign up

Export Citation Format

Share Document