Predicting Monthly Streamflow Using a Hybrid Wavelet Neural Network: Case Study of the Çoruh River Basin

Abstract

In this study, a hybrid model combining discrete wavelet transforms (WTs) and artificial neural networks (ANNs) is used to estimate the monthly streamflow. The WT-ANN hybrid model was developed using the Daubechies main wavelet to predict the streamflow for three gauging stations on the Çoruh river basin one month in advance, with different combinations of air temperature, precipitation, and streamflow variables, and their wavelet transformations. Four different hybrid WT-ANN models were generated and compared with four different conventional ANN models. The dataset was chronologically divided into training, validation, and testing data. The results indicated that the WT-ANN hybrid models performed better than the traditional ANN models for all three stations. Furthermore, the chronologically divided dataset was used to examine the effects of changes in hydrological data over time on model performance. In conclusion, model performances in the training period deteriorated during the validation and testing periods due to structural changes in the hydrological data.

Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

“…At present, biometric identification technology is widely used in daily life. For example, nowadays, almost all cell phones have fingerprint unlocking and face unlocking, and when using a cell phone, more and more people no longer enter the unlocking password but press the fingerprint or use the face to the camera can be unlocked directly [14][15][16]. Gradually, the password unlocking method on cell phones has been replaced by two unlocking methods: fingerprint unlocking and face unlocking.…”