Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability

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A conceptual runoff model mathematically expresses hydrological phenomena originating from spatial and temporal changes based on physical laws; thus, it can describe causal relationships for runoff changes. However, these models are difficult to run, owing to their complex structures and the need for a large amount of data. The development of runoff models using deep neural networks (DNN), which belong to the category of empirical models, is continuously increasing owing to the advantages of DNN, such as the modeling convenience and high prediction performance. However, DNN–based runoff models have difficulty reflecting topographical and spatial characteristics, such as land cover and physical soil characteristics, because they mainly depend on meteorological data. The objective of the present study was to overcome the limitation of data use that depends on meteorological data and to reflect topographical characteristics in DNN–based runoff models by suggesting a data creation methodology that reflects spatial characteristics. This paper proposes a methodology for producing two types of two–dimensional features—surface flow features (SFF) and base flow features (BFF)—using the Soil Conservation Service curve number and groundwater level. To determine the applicability of the features, the daily runoff was simulated using the generated features as input data of the convolutional neural network (CNN), which is a type of DNN. The CNN architecture was improved to have a multi–input structure and derive continuous variables, because the ordinary CNN was difficult to apply in this study. After the CNN was trained, the daily runoff was simulated, and the applicability of the features as input data of the DNN was evaluated. The daily runoff predictions of the CNN using the SFF and BFF exhibited moderate levels. This indicates that the SFF and BFF have sufficient value as input data of the CNN.