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Hybrid wavelet transform - MLR and ANN models for river flow prediction: Case study of Brahmaputra river (Pancharatna station)

Khandekar Sachin Dadu, Aswar Dinesh Shrikrishna, Sabale Pandurang Digamber, Khandekar Varsha Sachin, Bajad Mohankumar Namdeorao, Khaple Shivakumar

Przegląd Naukowy Inżynieria i Kształtowanie Środowiska

2024

Vol. 33, No. 1

69--94

In this research, discrete wavelet transform (DWT) is combined with MLR and ANN to develop WMLR and WANN hybrid models, respectively, for the Brahmaputra river (Pancharatna station) flow forecasting. Daily flow data for the period of 10 year were decomposed (up to fifth level) into detailed and approximation coefficients (using Daubechies wavelets db1, db2, db3, db8 and db10) which were fed as input to MLR and ANN to get the predicted discharge values two days, four days, seven days and 14 days ahead. For all lead times, the WMLR-db10 model was found to be superior as compared to WANN-db1, WANN-db2, WANN-db3, WANN-db8, WMLR-db1, WMLR-db2, WMLR-db3, WMLR-db8 and single MLR and ANN models. During testing period, the values of determination coefficient (R2) and RMSE for WMLR-db10 model for two-, four-, seven- and 14-day lead time were found to be, respectively, 0.996 (751.87 m3·s–1), 0.991 (1,174.80 m3·s–1), 0.984 (1,585.02 m3·s–1), and 0.968 (2,196.46 m3·s–1). Also, it was observed that for lower order wavelets (db1, db2, db3) WANN’s performance was better, and for higher order wavelets (db8, db10) WMLR’s performance was better. Correspondingly, it was observed that all hybrid models’ efficiency increased with increase in the decomposition level.

Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm

Schimmack M., Mercorelli P.

International Journal of Electronics and Telecommunications

2016

Vol. 62, No. 1

15--21

This article deals with the noise detection of discrete biosignals using an orthogonal wavelet packet. In specific, it compares the usefulness of Daubechies wavelets with different vanishing moments for the denoising and compression of the digitalised biosignals in case of surface electromyography (sEMG) signals. The work is based upon the discrete wavelet transform (DWT) version of wavelet package transform (WPT). A noise reducing algorithm is proposed to detect unavoidable noise in the acquired data in a model independent way. The noise of a signal sequence will be defined by a seminorm. This method was developed for a possible observation during a fracture healing period. The proposed method is general for signal processing and its design was based upon the wavelet packet.

Badania symulacyjne procesu usuwania szumów z sygnału pomiarowego z wykorzystaniem analizy falkowej

Makieła W., Janusiewicz A.

Zeszyty Naukowe Politechniki Poznańskiej. Budowa Maszyn i Zarządzanie Produkcją

2010

Nr 13

51-56

W dotychczasowej praktyce w analizie sygnałów pomiarowych uzyskiwanych w wyniku pomiarów zarysów nierówności powierzchni szeroko wykorzystuje się zasadę transformacji Fouriera. Znalazło to szczególne zastosowanie w analizie pomiarów okrągłości i walcowości. Z tego względu, że w wielu dziedzinach metrologii do oceny sygnałów stosuje się z dużym powodzeniem transformację falkową, podjęto prace nad aplikacją tej metody do oceny zarysów struktury geometrycznej powierzchni. Niniejszy referat poświęcony jest problematyce dekompozycji i rekonstrukcji sygnału po eliminacji szumu dla różnych postaci falki bazowej. W referacie przedstawiono przykłady obliczeń dla wybranego sygnału pomiarowego będącego wynikiem pomiaru zarysu okrągłości.

In hitherto practice an analysis of signals obtained through measurements of geometrical surface structure is performed usually by Fourier transform principle. Such approach is applied particularly in analysis of roundness and cylindricity measurements. However, in numerous branches of metrology evaluation of signals is performed by wavelet transform. Therefore efforts aiming at application of this method to evaluation of geometrical surface structure have been taken. The paper concerns problems of signal decomposition and reconstruction after its denoising for different basic wavelets. In the paper examples of calculations fof selected roundness measurement signal are given.