Water Level Prediction based on Conditional Generation Adversarial Network

Improving the prediction accuracy of water levels is of great significance to water resources scheduling, flood control, and disaster reduction. Deep learning-based recurrent neural networks variants have proved to be a powerful algorithmic model for water level prediction. However, few people use generation adversarial network to mine water level time series information. In this work, we propose a simple and effective method (termed WLP-ForGAN) for watershed multi-point Water Level Prediction based on Conditional Generative Adversarial Networks. Through the adversarial training of the generator and discriminator, the predicted data is constantly close to the real data, to realize the prediction of time series, and improve the utilization of limited data information of the model. The attention module is embedded in the WLP-ForGAN network structure of the generator and discriminator, so that the WLP-ForGAN model can autonomously assign the attention weight value to the input series vectors according to the hydrological information in the input features. Extensive experiments on real datasets show that the proposed WLP-ForGAN consistently outperforms commonly used models, which proves the effectiveness of the proposed method.