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This study employed a convolution neural network (CNN) model, hitherto used only for solving classification problems, with two-dimensional input data to predict the pollution loads and evaluate the CNN model’s applicability. A CNN model generally requires two-dimension input data, such as photographs in previous studies. However, this study’s CNN model necessitates the numerical images that reflect hydrological phenomena due to the nature of the study. A hydrological image was used as the input data for the CNN model in this study to address this issue. The last layer of the CNN model was also transformed into a linear function to derive the continuous variable. As a result, the Pearson correlation coefficient, which represents the relationship between the measured and predicted values, demonstrated a Biochemical Oxygen Demand (BOD) load model of 0.94 and a Total Phosphorus (TP) load model of 0.87. Nash–Sutcliffe efficiency was used to evaluate the model performance; the BOD load model was 0.83, while the TP load model was 0.79, respectively, indicating good performance. These results demonstrate that the hydrological images led to stable model learning and generalization, and the proposed CNN model is suitable for predicting the pollution load, with potential future applications in various fields.

agriculture

Applicability Evaluation of the Hydrological Image and Convolution Neural Network for Prediction of the Biochemical Oxygen Demand and Total Phosphorus Loads in Agricultural Areas