@article{article_1160254, title={Investigation Of Lake Water Level Forecasting Performances Of Subband Decomposition Techniques}, journal={Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi}, volume={38}, pages={657–675}, year={2022}, author={Latifoğlu, Levent and Haktanir, Tefaruk}, keywords={Göl su seviyesi tahmini, Yapay Sinir Ağları, Ampirik Kip Ayrıştırma, Tekil Spektrum Analizi}, abstract={In this study, hybrid methods have been developed for estimation of monthly average water level of a natural lake in the coming months from the next one to the sixth month ahead. Lake water level data were preprocessed using Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD), Singular Spectral Analysis (SSA) techniques and these subband signals were applied to the input data of Artificial Neural Networks (ANN). Thus, three different hybrid models were obtained and the prediction performance of these models was analyzed. According to obtained results, it was observed that the hybrid approaches obtained with the preprocessing methods applied to the water level data improved the model performance and EMD-ANN and SSA-ANN hybrid models were found to better predict average monthly lake water levels for a forecast period of one to six months than the ANN and DWT-ANN model}, number={3}, publisher={Erciyes Üniversitesi}