Hybrid model of generative adversarial network and Takagi-Sugeno for multidimensional incomplete hydrological big data prediction

The processing of rainfall-runoff transmission in the catchment area is a very complex phenomenon, such as temporal and spatial changes of the catchment characteristics and uncertainties of rainfall patterns. To handle this challenge, data-driven hydrologic models emerge rapidly for the rainfall runoff prediction. However, the incomplete hydrological data constrain the development of digital hydrologic model. This article proposes a rainfall-runoff prediction method of coupling generative adversarial network (GAN) and Takagi-Sugeno (T-S) fuzzy model, in which the GAN is used to generate the data for completing the incomplete hydrological data, and the T-S fuzzy model is utilized for forecasting the rainfall runoff. The presented method is examined by real data in Huaihe basin just considering the precipitation and streamflow. Experiments show that the model combined GAN and T-S can achieve satisfactory prediction results.