Streamflow Forecasting by CNN-GRU Model

• Main Authors: LIAO, PEN-MIN, 廖本閔
• Other Authors: CHEN, CHANG-SHIAN
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/8rs76r

碩士 === 逢甲大學 === 水利工程與資源保育學系 === 107 === During the last two decades, the application of artificial intelligence in the field of flood forecasting has increased noticeably. Since the information of flood forecasting is the most important part of disaster management, also the emergency response and the mechanism of Recurrent Neural Network (RNN) include the behavior of the time series, this study attempt to adopt the Gated Recurrent Unit (GRU) which is a type of RNN used to develop a rainfall-runoff model for the mentioned purpose above. In this research RNN is using Gated Recurrent Unit (GRU). In each field, applicability of GRU is still in researching. Thereby, this paper will discuss the application GRU in the flood forecast. In order to improve the prediction accuracy of the GRU, the data is processed by using the Convolutional Neural Network (CNN) and then input into the GRU for prediction, called CNN-GRU. In the past, most studies used to extract every rainfall from the data before learning artificial neural networks for flood flow prediction. However this study will use a different approach, because GRU cell can remember the status from past. In addition, optimal hyperparameters setting for artificial neural networks will be found by genetic algorithm (GA) to modeling Dali River hourly rainfall-runoff model. Evaluation indicators show that CNN-GRU is better than GRU, the evaluation indicators show that CNN-GRU is better than GRU, because CNN-GRU uses CNN to extract eigenvalues from input data before using GRU for prediction.