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

Chul Min Song and Jin Soo Kim
Additional contact information
Chul Min Song: Department of Policy for Watershed Management, The Policy Council for Paldang Watershed, Yangpyeong 12585, Korea
Jin Soo Kim: Department of Agricultural Rural Engineering, Chungbuk National University, Choengju 28644, Korea

Agriculture, 2020, vol. 10, issue 11, 1-20

Abstract: 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.

Keywords: convolution neural network; hydrological image; biochemical oxygen demand load; total phosphorus load; prediction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/10/11/529/pdf (application/pdf)
https://www.mdpi.com/2077-0472/10/11/529/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:10:y:2020:i:11:p:529-:d:440567

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().