Application of LSTM Model to Water Stage Forecasting

• Main Authors: TSAI, CHENG-HAN, 蔡承翰
• Other Authors: CHEN, CHANG-SHIAN
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/mzv86q

碩士 === 逢甲大學 === 水利工程與資源保育學系 === 107 === In recent years, abnormal weather conditions are observed and the heavy rainfall events are increasing, which is different from the previous disaster characteristics. The river stage rises rapidly and various areas have suffered from floods, resulting in losses of life and property. In previous studies, neural network was often used for prediction but ordinary neural networks cannot preserve the previous information during the prediction which limits the long-term prediction ability. Recurrent Neural Network (RNN) is a suitable choice to overcome this limitation. An RNN model has internal self-looped cells, allowing the RNN to remember information that time series conveyed. RNN was facing gradient explosion and gradient vanish while doing deep learning. To overcome the problem of RNN this study used Long Short-Term Memory model as main structure to build a precipitation-water stage forecasting model. According to the way that we used data, we build three categories model to investigate the different of using average rainfall and distributed rainfall as training data and explore the possibility of application upstream hydrological data as training data to forecast downstream water stage. All the models have good forecasting performance, indicating that the proposed forecasting models had potential to be used to other watersheds.