Improved Convolutional Neural Network and its Application in Non-Periodical Runoff Prediction

Xu, Yichao; Liu, Yi; Jiang, Zhiqiang; Yang, Xin; Wang, Xinying; Zhang, Yunkang; Qin, Yangyang

doi:10.1007/s11269-022-03346-3

Cited by 13 publications

(1 citation statement)

References 47 publications

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“…It borrows ideas from 2D CNN for image processing, but is specifically used for one-dimensional data. CNN detect local features in the input sequence through convolutional operations, similar to image regions in 2D CNN (Xu et al 2022). The convolution kernel is slid over the sequence to compute the local features at each location.…”

Section: Cnn Lstm and Grumentioning

confidence: 99%

A Hybrid Deep Learning Model for Short-Term Hydropower Generation Prediction Incorporating Hydrometeorological Factors

Peng,

Jiang,

Yao

et al. 2023

Preprint

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Hydropower is a clean and renewable source of energy, reliable forecasts of hydropower generation are critical for reservoir management and efficient use of water resources. In this study, a Double Attention mechanism-Convolutional Neural Network-Bidirectional Gated Recurrent Unit(DAC-BiGRU) hybrid deep learning model considering hydrometeorological factors is proposed for predicting short-term hydropower generation. DAC-BIGRU first extracts the local features of the input data using a 1-dimensional CNN and weights the features using an attentional mechanism instead of a pooling layer. Then passes features to the BiGRU layer for time series modelling. Finally performs the attentional mechanism weighting again and maps the results to the final output via a dense layer. The Qiamusa and Aertashi hydropower stations on the mainstem of the Yarkant River in Northwest China are used as the study object for cross-training and validation. The Pearson Correlation Coefficient and Maximum mutual Information Coefficient were used to determine hydropower generation, steamflow and soil temperature for the past 7 days as input features.Through rigorous and reasonable evaluation, the results demonstrate that: Root Mean Squared Error (RMSE) of DAC-BiGRU on the test set decreased by 8.8% on average compared with CNN-LSTM and CNN-GRU. DAC-BiGRU model performance was significantly better than CNN-LSTM, CNN-GRU, Long Short-Term Memory (LSTM) and Support Vector Machine (SVM). In addation, relative error of DAC-BiGRU mainly occurred from October to March, RMSE of the DAC-BiGRU decreased by about 3% after adding streamflow and soil temperature as factors. The prediction performance of DAC-BiGRU for the next 3–7 days began to decline significantly.

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Section: Cnn Lstm and Grumentioning

confidence: 99%