Asst. Prof. Dr. Huseyin Cagan Kilinc
Istanbul Esenyurt University, Engineering Faculty, Civil Engineering Department, Istanbul, Türkey
Periodic river flow measurements are required to ensure sustainable water resources. For this, different estimation methods are required. In this study, Deep Learning (DL) and Aksu River flows were estimated by LSTM (Long-Short Term Memory) neural network, which is one of the Artificial Intelligence methods. In the study, the data belonging to Başpınar Flow Measurement Station (FMS) (D20A002) on Aksu River between 2000-2019 were used as input for analysis. In addition, the performance effect of Single Spectrum Analysis (TSA) on LSTM was examined. Adam, Adamax and AdaGrad algorithms were applied to the TSA-LSTM model. The most accurate estimation model has been determined by comparing the estimate and actual values. It has been observed that the Adamax optimizer provides the best performance in flow estimation. TSA-LSTM model coefficient (R2) determination was found to be 0.9851 during the test phase. When the obtained results were examined, it was seen that the TSA-LSTM model gave better results in estimating flow studies.
Keywords: LSTM, Singular Spectrum, Stream Flow
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