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1

Selection of level and type of decomposition in predicting suspended sediment load using wavelet neural network

Reddy Beeram Satya Narayana, Pramada S. K., Roshni Thendiyath

Acta Geophysica

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2022

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Vol. 70, no 2

847--857

EN

The advancements in artificial intelligence play a significant role in solving the problems of researchers and engineers to develop prediction models with higher accuracy over the analytical and numerical models. The wavelet ensemble artificial intelligence model has a widespread application in forecasting hydrological datasets. The signal decomposition type, level and the mother wavelet affect the model performance in wavelet-based approaches. The present analysis focuses on studying the significance of the level and type of decomposition in wavelet transform for pre-processing the input variables to predict the target variable. In this work, to forecast seasonal suspended sediment load of the Kallada River basin in Kerala, two types of decomposition with decomposition levels ranging from 2 to 7 were adopted using seasonal flow data (wet and dry seasons). To rank the WANN models, compromise programming was adopted using the results based on statistical performance indicators and compared with the performance of the conventional FFNN model. From the accuracy assessment and ranking, type-2 with 5th level decomposition can capture the actual periodicity of the signal and predict the suspended sediment load with higher accuracy. It also shows the capability to predict the extreme events of time series.

2

Classical versus deep learning methods for anomaly detection in ECG using wavelet transformation

Osowski Stanislaw, Gołgowski Maciej

Przegląd Elektrotechniczny

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2021

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R. 97, nr 6

72--76

EN

The paper describes and compares two forms of wavelet transformation: discrete (DWT) and continuous (CWT) in the analysis of electrocardiograms (ECG) to detect the anomaly. The anomalies have been limited to two types: cardiac and congestive heart failure. Two independent approaches to the problem have been considered. One is based on discrete wavelet transformation and feature generation based on statistical parameters of the results of the transformed ECG signals. These descriptors, after selection, are delivered as the input attributes to different classifiers. The second approach applies continuous wavelet transformation of ECG signals and the resulting two-dimensional image formed in time-frequency dimensions represents the input to the convolutional neural network, which is responsible for the generation of the diagnostic features and final classification. The experiments have been performed on the publically available database Complex Physiologic Signals PhysioNet. The calculations have been done in Python. The results of both approaches: DWT and CWT have been discussed and compared.

PL

Artykuł predstawia dwa podejścia do wykrywania anomalii w sygnalach ECG. Jako anomalie rozważane są: arytmia i zastoinowa niewydolność serca. Podstawą analizy jest sygnał ECG poddany transformacji falkowej w dwu postaciach: transformacja dyskretna oraz transformacja ciągła. W przypadku transformacji dyskretnej sygnał ECG poddany jest dekompozycji falkowej na kilku poziomach a wyniki tej dekompozycji (sygnały szczegółowe i sygnał aproksymacyjny ostatniego poziomu) podlegają opisowi statystycznemu tworząc zbiór deskryptorów numerycznych – potencjalnych cech diagnostycznych. Po przeprowadzonej selekcji stanowią one atrybuty wejściowe dla zespołu 9 klasyfikatorów. W drugim podejściu sygnał ECG jest poddany ciągłej transformacji falkowej generując dwuwymiarową macierz w postaci obrazu. Zbiór takich obrazów podawany jest na wejście głębokiej sieci neuronowej CNN, która w jednej strukturze dokonuje jednocześnie generacji cech diagnostycznych i klasyfikacji. Eksperymenty numeryczne przeprowadzone zostały na ogólnie dostępnej bazie danych Complex Physiologic Signals PhysioNet. Wyniki eksperymentów wykazały przewagę podejścia wykorzystujacego dyskretną transformację falkową.

3

Stress feld estimation by the geoid undulations of the Samos Kuşadası Bay and implications for seismogenic behavior

Oruç Bülent, Balkan Emir

Acta Geophysica

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2021

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Vol. 69, no. 3

1137--1149

EN

The strong earthquake with magnitude 6.9 occurred ofshore at the northernmost edge of the Samos Island and was strongly felt in the north Aegean islands and İzmir metropolitan city. In this study, the effective elastic thicknesses of the lithosphere and seismogenic layer thickness were correlated with each other in order to understand the nature of the earthquakes. We determined that the upper and lower depth limits of seismogenic layer are in a range of 5–15 km, meaning that only the upper crust is mostly involved in earthquakes in the study area. The fact that seismogenic layer and effective elastic thicknesses are close to each other indicates that the earthquake potential may be within the seismogenic layer. Following that, we estimate the stress feld from the geoid undulations as a proxy of gravity potential energy in order to analyze the amplitude and orientation of the stress vectors and seismogenic behavior implications. The discrete wavelet transform has been carried out to decompose the isostatic residual gravity anomalies into horizontal, vertical and diagonal detail coefcients. The results delineated edges of gravity anomalies that reveal some previously unknown features.

4

Adaptive iterative deblending of simultaneous source seismic data based on sparse inversion

Wei Yajie, Cao Jingjie, Huang Xiaogang, Chen Xue, Cai Zhicheng

Acta Geophysica

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2021

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Vol. 69, no. 2

497--507

EN

Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efciency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: fltering method and sparse inversion method. Compared with the fltering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the efective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the efective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefcients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefcient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efciency, which is suitable for large-scale seismic data processing.

5

Automatic diagnosis of atrial fibrillation based on online RR interval analysis

Sokol Yevgen, Shapov Pavel, Shyshkin Mykhailo, Koval Sergiy, Tomashevskyi Roman

Zeszyty Naukowe Instytutu Pojazdów / Politechnika Warszawska

|

2019

|

z. 2/120

53--60

EN

The article presents the results of research of the tachogram of the cardiac signal with areas of atrial fibrillation. Using the wavelet transform to analyse non-stationary processes, it was shown that dispersion decomposition is an instantaneous correlation between the wavelet spectrum and has two implementations: frequency non-stationarity models - for frequency and time estimates, the ability to specify a parameter that should be relevant for the onset of atrial fibrillation. The calculation of this parameter can be used to detect fibrillation during the online recording of RR intervals.

PL

W artykule przedstawiono wyniki badań tachogramu sygnału sercowego z obszarami migotania przedsionków. Wykorzystanie transformacji falkowej do analizy procesów niestacjonarnych wykazało, że dekompozycja dyspersji jest chwilową korelacją między widmem falkowym i ma dwie implementacje: modele niestacjonarne częstotliwości - dla szacunków częstotliwości i czasu, możliwość określenia parametru, który powinien mieć znaczenie dla początku migotania przedsionków. Obliczenie tego parametru może być wykorzystane do wykrycia migotania podczas rejestracji online odstępów RR.

6

Complex morlet wavelet design with global parameter optimization for diagnosis of industrial manufacturing faults of tapered roller bearing in noisycondition

Deák Krisztián, Kocsis Imre

Diagnostyka

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2019

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Vol. 20, No. 2

77--86

EN

Detecting manufacturing defects of bearings are difficult because of their unique topography. To find adequate methods for diagnosis is important because they could be responsible for serious problems. Wavelet transform is an efficient tool for analyzing the transients in the vibration signal. In this article we are focusing on industrial grinding faults on the outer ring of tapered roller bearings. Nine different real-valued wavelets, Symlet-2, Symlet-5, Symlet-8, Daubechies (2, 6, 10, 14), Morlet and Meyer wavelets are compared to a designed complex Morlet wavelet according to the Energy-to-Shannon-Entropy ratio criteria to determine which is the most efficient for detecting the manufacturing fault. Parameters of the complex Morlet wavelet are adjustable, thus, it has more flexibility for feature extraction. Genetic algorithm is applied to optimize the center frequency and the bandwidth of the designed wavelet. A sophisticated filtering procedure through multi-resolution analysis is applied with autocorrelation enhancement and envelope detection. To determine the efficiency of the designed wavelet and compare to the other wavelets, a test-rig was constructed equipped with high-precision sensors and devices. The designed wavelet is found to be the most effective to detect the manufacturing fault. Therefore, it has the capacity for an industrial testing procedure.

7

Application of wavelet – neural method to detect backlash zone in electromechanical systems generating noises

Tomczyk Marcin, Plichta Anna, Mikulski Mariusz

Applied Computer Science

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2019

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Vol. 15, no 4

93--108

EN

This paper presents a method of identifying the width of backlash zone in an electromechanical system generating noises. The system load is a series of rectangular pulses of constant amplitude, generated at equal intervals. The need for identification of the backlash zone is associated with a gradual increase of its width during the drive operation. The study uses wavelet analysis of signals and analysis of neural network weights obtained from the processing without supervised learning. The time-frequency signal representations of accelerations of the mechanical load components were investigated.

8

Application of image analysis to the identification of mass inertia momentum in electromechanical system with changeable backlash zone

Tomczyk Marcin, Plichta Anna, Mikulski Mariusz

Applied Computer Science

|

2019

|

Vol. 15, no 3

87--102

EN

This paper presents a new method of identification of inertia moment of reduced masses on a shaft of an induction motor drive being a part of an electromechanical system. The study shows the results of simulations performed on the tested model of a complex electromechanical system during some changes of a backlash zone width. An analysis of wavelet scalograms of the examined signals carried out using a clustering technique was applied in the diagnostic algorithm. The correctness of the earliest fault detection has been verified during monitoring and identification of mass inertia moment for state variables describing physical quantities of a tested complex of the electromechanical system.

9

Spline-wavelet bent robust codes

Levina Alla, Ryaskin Gleb, Zikratov Igor

Annals of Computer Science and Information Systems

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2019

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Vol. 18

227--230

EN

This paper presents an application of spline-wavelet transformation and bent-functions for the construction of robust codes. To improve the non-linear properties of presented robust codes, bent-functions were used. Bent-functions ensure maximum non-linearity of functions, increasing the probability of detecting an error in the data channel. In the work different designs of codes based on wavelet transform and bent-functions are developed. The difference of constructions consists of using different grids for wavelet transformation and using different bent-functions. The developed robust codes have higher characteristics compared to existing. These codes can be used for ensuring the security of transmitted information.

10

Long term monthly streamfow forecasting in humid and semiarid regions

Fouchal Amel, Souag Gamane Doudja

Acta Geophysica

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2019

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Vol. 67, no. 4

1223--1240

EN

Long-term monthly streamfow forecasting has great importance in the water resource system planning. However, its modelling in extreme cases is difcult, especially in semiarid regions. The main purpose of this paper is to evaluate the accuracy of artifcial neural networks (ANNs) and hybrid wavelet-artifcial neural networks (WA-ANNs) for multi-step monthly streamfow forecasting in two diferent hydro-climatic regions in Northern Algeria. Diferent issues have been addressed, both those related to the model’s structure and those related to wavelet transform. The discrete wavelet transform has been used for the preprocessing of the input variables of the hybrid models, and the multi-step streamfow forecast was carried out by means of a recursive approach. The study demonstrated that WA-ANN models outperform the single ANN models for the two hydro-climatic regions. According to the performance criteria used, the results highlighted the ability of WAANN models with lagged streamfows, precipitations and evapotranspirations to forecast up to 19 months for the humid region with good accuracy [Nash–Sutclife criterion (Ns) equal 0.63], whereas, for the semiarid region, the introduction of evapotranspirations does not improve the model’s accuracy for long lead time (Ns less than 0.6 for all combinations used). The maximum lead time achieved, for the semiarid region, was about 13 months, with only lagged streamfows as inputs.

11

Effective denoising of magnetotelluric (MT) data using a combined wavelet method

Ling Zhenbao, Wang Peiyuan, Wan Yunxia, Li Tonglin

Acta Geophysica

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2019

|

Vol. 67, no. 3

813--824

EN

Noise interference, especially from human noise, seriously affects the quality of magnetotelluric (MT) data. Strong human noise distorts the apparent resistivity curve, known as the near-source effect, causing poor reliability of MT data inversion. Based on analyzing the frequency characteristics of human noise resulting from the surrounding environment, a new waveletbased denoising method is proposed for both synthetic and real MT data in this paper. The new technique combines multiresolution analysis with a wavelet threshold algorithm based on Bayes estimation and has a remarkable effect on denoising at all band frequencies. The multi-resolution analysis method was employed to reduce long-period noise, and a wavelet threshold algorithm was used to eliminate strong high-frequency noise. In this research, the improved algorithm was assessed via simulated experiments and field measurements with regard to the reduction in human noises. This study demonstrates that the new denoising technique can increase the signal-to-noise ratio by at least 112% and provides an extensive analysis method for mineral resource exploration.

12

Analiza możliwości wykorzystania transformacji falkowej do detekcji niezajętych pasm częstotliwości

Bronk K., Rutkowski D., Wereszko B., Wereszko K., Żurek K.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

|

2018

|

nr 6

463--466, CD

PL

Celem pracy była implementacja oraz wykonanie badań efektywności wybranej metody wykrywania niezajętych zasobów częstotliwości, opartej na wyznaczaniu entropii sygnału z wykorzystaniem analizy falkowej. W referacie przedstawiono podstawy teoretyczne rozważanych zagadnień, wyniki przeprowadzonych badań laboratoryjnych i ich analizę oraz wnioski.

EN

The aim of the paper is the implementation and efficiency evaluation of a method selected for the detection of the unused frequency resources. This method is based on the determination of signal entropy using wavelet analysis. The paper contains the theoretical fundamentals of the approach considered, the results obtained and their analysis as well as the final conclusions.

13

Image encryption algorithms based on wavelet decomposition and encryption of compressed data in wavelet domain

Świercz E.

Przegląd Elektrotechniczny

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2018

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R. 94, nr 2

79--83

EN

The main purpose of this paper is the evaluation of the developed image encryption algorithm based on wavelet decomposition of images. Encryption algorithms DES (Data Encryption Standard) and AES (Advanced Encryption Standard) are used only for encryption of detail coefficients of the wavelet decomposition and encrypted images are the result of the inverse wavelet transform. Compressed data is also examined in the encryption process. This encryption approach is implemented in Matlab environment.

PL

Głównym celem tego artykułu jest ocena opracowanego algorytmu szyfrowania obrazów opartego o dekompozycję falkową obrazów. Algorytmy szyfrowania DES (Data Encryption Standard) i AES (Advanced Encryption Standard) są wykorzystane do szyfrowania tylko współczynników detali dekompozycji falkowej a zaszyfrowane obrazy są wynikiem odwrotnej transformacji falkowej. Skompresowane dane są również badane w procesie szyfrowania. Ten proces szyfrowania jest implementowany w środowisku Matlab.

14

Composition of wavelet and Fourier transforms

Ziółko M., Witkowski M., Gałka J.

Mathematica Applicanda

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2018

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Vol. 46, no. 1

159--168

EN

The paper presents the basic properties of the serial composition of two transformations: wavelet and Fourier. Two types of transformations were obtained because wavelet and Fourier transformations do not commute. The consequences of a phenomenon known as a "wavelet crime" are presented. Using wavelets with compact support in the frequency domain (e.g. Meyer wavelets) leads to the representation of signals as sparse matrices. Speech signals were used to test the presented transforms.

PL

W pracy przedstawione są podstawowe własności szeregowego złożenia dwóch transformacji: falkowej i Fouriera. Uzyskano dwa rodzaje transformacji ponieważ transformacje falkowe i Fouriera nie są przemienne. Przedstawione są konsekwencje zjawiska zwanego "przestępstwem falkowym". Zastosowanie falek ze zwartymi nośnikami w dziedzinie częstotliwości (np. falki Meyera) prowadzi do reprezentacji sygnałów w postaci macierzy rzadkich. Sygnały mowy zostały użyte do przetestowania przedstawionych transformacji.

15

An expert system aided method of the blade vibration sources identification with blade-tip timing (BTT) signal decomposition

Borowczyk H., Rokicki E., Spychała J.

Journal of KONBiN

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2018

|

No. 48

341--358

EN

An expert system aided method of the blade-tip signal decomposition to the turbine blade vibration sources identification is presented. The method utilises a multivalued diagnostic model based on the discrete wavelet transform. Proposed algorithm consists of four stages: signal decomposition into low- and high-frequency components (approximations and details), approximations and details parameterization, multi-valued encoding of parameters obtained at the second stage, an expert system use of the turbine blade vibration sources identification.

PL

W artykule przedstawiono metodę ekspertowego wspomagania identyfikacji źródeł drgań łopatek turbiny na podstawie dekompozycji sygnału generowanego przez wierzchołki łopatek. Zastosowano wielowartościowy model diagnostyczno-decyzyjny uzyskany z wykorzystaniem transformaty falkowej. Proponowany algorytm metody składa się z czterech faz: falkowa dekompozycja sygnału na składowe niskoczęstotliwościowe (tzw. aproksymacje) i wysokoczęstotliwościowe (tzw. detale), parametryzacja aproksymacji i detali, wielowartościowe kodowanie parametrów uzyskanych w drugiej fazie, zastosowanie systemu ekspertowego do identyfikacji źródeł drgań łopatek.

16

Efficiency assessment of wavelet transforms and wavelets for damage localization in beams using shearography

Kroworz A., Katunin A., Lopes H., Araújo Dos Santos J.

Diagnostyka

|

2018

|

Vol. 19, No. 4

71--79

EN

Non-destructive testing of engineering structures and elements in operation is one of the crucial steps in recently introduced design philosophies: damage tolerance and condition-based maintenance. Therefore, it is important to provide effective non-destructive testing methods, which are able to detect and identify a possible damage in early stage of its development. One effective testing method, which still gains its popularity in various industrial applications, is shearography. Although, shearography is sensitive to various types of structural damage and flaws, this sensitivity can be significantly improved by applying advanced post-processing algorithms to raw data obtained from measurements. An excellent candidate for such an improvement is the wavelet analysis, due to its very high sensitivity to smallest signal disturbances. This study presents results of comparative analysis of various wavelet transforms and various wavelets in order to analyse their sensitivity to damage. The improvement in damage detectability is verified using experimental data.

PL

Badania nieniszczące inżynierskich struktur i elementów będących w eksploatacji są jednym z najważniejszych kroków w obecnie zapoczątkowanych filozofii projektowania: tolerancji uszkodzenia oraz eksploatacji na podstawie stanu. Dlatego ważnym jest wykorzystanie efektywnych metod badań nieniszczących, które są zdolne do detekcji i identyfikacji możliwego uszkodzenia we wczesnej fazie jego rozwoju. Jedną z efektywnych metod badawczych, która wciąż zyskuje na popularności w różnych zastosowaniach przemysłowych, jest shearografia. Mimo że shearografia jest wrażliwa na różne rodzaje uszkodzeń i wad w strukturach, jej wrażliwość może być znacząco ulepszona poprzez zastosowanie zaawansowanych algorytmów przetwarzania do surowych danych wynikowych otrzymanych z pomiarów. Doskonałym kandydatem dla takiego polepszenia jest analiza falkowa ze względu na jej bardzo dużą wrażliwość na najmniejsze zaburzenia w sygnale. Niniejsza praca przedstawia wyniki analizy porównawczej różnych transformacji falkowych oraz różnych falek w celu analizy ich wrażliwości na uszkodzenia. Polepszenie wykrywalności uszkodzeń zostało zweryfikowane z wykorzystaniem danych eksperymentalnych.

17

Identification of the mass inertia moment in an electromechanical system based on wavelet–neural method

Tomczyk Marcin, Borowik Barbara, Borowik Bohdan

Applied Computer Science

|

2018

|

Vol. 14, no 2

96--111

EN

This paper presents the results of testing of a complex electromechanical system model. These results have been obtained for accepted in simulations the method of identifying an inertia moment of reduced masses on shaft of induction motor drive during the changes of a backlash zone width. The effectiveness of correct diagnostic conclusions enables coefficients analysis of testing signals wavelet expansion as well as weights of a supervised learning neural network. The earlier fault detection of five important state variables, which describe physical quantities of chosen complex electro-mechanical system has been verified for its correctness during the backlash zone width monitoring in the early stage of its gradual rise. The proposed here algorithm with mass inertia moment changes has proved to be an effective diagnostic method in the area of system changeable dynamic conditions and this has been shown in the resulting changes of backlash zone width.

18

Identification of a Backlash Zone in an Electromechanical System Containing Changes of a Mass Inertia Moment Based on a Wavelet–Neural Method

Tomczyk Marcin, Borowik Barbara, Mikulski Mariusz

Applied Computer Science

|

2018

|

Vol. 14, no 4

54--69

EN

In this article a new method of identification of a backlash zone width in a structure of an electromechanical system has been presented. The results of many simulations in a tested model of a complex electromechanical system have been taken while changing a value of a reduced masses inertia moment on a shaft of an induction motor drive. A wavelet analysis of tested signals and analysis of weights that have been obtained during a neural network supervised learning - have been applied in a diagnostic algorithm. The proposed algorithm of detection of backlash zone width, represents effective diagnostic method of a system at changing dynamic conditions, occurring also as a result of mass inertia moment changes.

19

Detection of Arrhythmia using Neural Network

Mohapatra S. K., Palo H. K., Mohanty M. N.

Annals of Computer Science and Information Systems

|

2018

|

Vol. 14

97--100

EN

There is an increase in cardio logical patients all over the world due to change in modern life style. It forces the medical researchers to search for smart devices that can diagnosis and predict the onset of cardiac problem before it is too late. This motivates the authors to predict Arrhythmia that can help both the patients and the medical practitioners for better healthcare services. The proposed method uses the frequency domain information which can represent the ECG signals of Arrhythmia patients better. Features representing the MIT-BIH Arrhythmia are extracted using the efficient Short Time Fourier Transform and the Wavelet transform. A comparison of these features is made with that of normal human being using Neural Network based classifier. Wavelet based features has shown an improvement of Accuracy over that of STFT features in classifying Arrhythmia as our results reveal. A Mean Square Error (MSE) of with wavelet transform has validated our results.

20

Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method

Piasecki A., Jurasz J., Adamowski J. F.

Acta Geophysica

|

2018

|

Vol. 66, no. 5

1093--1107

EN

Lake waters are a significant source of drinking water and contribute to the local economy (e.g. enabling irrigation, offering opportunities for tourism, waterways for transport, and meeting utility water demands); therefore, the ability to accurately forecast lake water levels is important. However, given the significant lack of research with respect to forecasting water levels in small lakes (i.e. 0.05 km2\area\10 km2), the present study sought to address this knowledge gap by testing a pair of hypotheses: (1) it is possible to forecast water levels in small surface lakes using artificial neural networks (ANN), and (2) better water-level forecasts will be obtained when the wavelet transform (WT) is used as an input data preprocessing tool. Based on an analysis of a case study in Lake Biskupinskie (1.16 km2) in Poland and based on a range of model performance statistics (e.g. mean absolute error, root mean square error, mean squared error, coefficient of determination, mean absolute percentage error), both hypotheses were confirmed for monthly forecasting of lake water levels. ANNs provided good forecasting results, and WT pre-processing of input data led to even better forecasts. Additionally, it was found that meteorological variables did not have a significant impact in forecasting water-level fluctuations. In light of the results and the limited scope of the present study, proposed future research directions and problems to be resolved are discussed.