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Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique

Author

Listed:
  • So Young Woo

    (School of Civil and Environmental Engineering, College of Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Chung Gil Jung

    (Agricultural and Water Resources Engineering, Texas A&M AgriLife Research Center at El Paso, 1380 A&M Circle, El Paso, TX 79927-5020, USA)

  • Ji Wan Lee

    (School of Civil and Environmental Engineering, College of Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

  • Seong Joon Kim

    (School of Civil and Environmental Engineering, College of Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea)

Abstract

In this study, we evaluated the aquatic ecosystem health (AEH) with five grades (A; very good to E; very poor) of FAI (Fish Assessment Index), TDI (Trophic Diatom Index), and BMI (Benthic Macroinvertebrate Index) using the results of SWAT (Soil and Water Assessment Tool) stream water temperature (WT) and quality (T-N, T-P, NH 4 , NO 3 , and PO 4 ). By applying Random Forest, one of the machine learning algorithms for classification analysis, each AEH index was trained and graded from the SWAT results. For Han river watershed (34,418 km 2 ) in South Korea, the 8 years (2008~2015) observed AEH data of Spring and Fall periods at 86 locations from NAEMP (National Aquatic Ecological Monitoring Program) were used. The AEH was separately trained for Spring (FAI s , TDI s , and BMI s ) and Fall (FAI a , TDI a , and BMI a ), and the AEH results of Random Forest with SWAT (WT, T-N, T-P, NH 4 , NO 3 , and PO 4 ) as input variables showed the accuracy of 0.42, 0.48, 0.62, 0.45, 0.4, and 0.58, respectively. The reason for low accuracy was from the weak strength of the individual trees and high correlation between the trees composing the Random Forest due to the data imbalance. The AEH distribution results showed that the number of Grade A of total FAI, TDI, and BMI were 84, 0, and 158 respectively and they were mostly located at the upstream watersheds. The number of Grade E of total FAI, TDI, and BMI were 4, 50, and 13 and they were shown at downstream watersheds.

Suggested Citation

  • So Young Woo & Chung Gil Jung & Ji Wan Lee & Seong Joon Kim, 2019. "Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique," Sustainability, MDPI, vol. 11(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3397-:d:241476
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    References listed on IDEAS

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    3. Vincenzi, Simone & Zucchetta, Matteo & Franzoi, Piero & Pellizzato, Michele & Pranovi, Fabio & De Leo, Giulio A. & Torricelli, Patrizia, 2011. "Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy," Ecological Modelling, Elsevier, vol. 222(8), pages 1471-1478.
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    1. Wonjin Kim & Seongjoon Kim & Jinuk Kim & Jiwan Lee & Soyoung Woo & Sehoon Kim, 2022. "Assessment of Long-term Groundwater Use Increase and Forest Growth Impact on Watershed Hydrology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5801-5821, December.
    2. Seoro Lee & Jonggun Kim & Gwanjae Lee & Jiyeong Hong & Joo Hyun Bae & Kyoung Jae Lim, 2021. "Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    3. Siyeon Kim & Jiwan Lee & Seol Jeon & Moonyoung Lee & Heejin An & Kichul Jung & Seongjoon Kim & Daeryong Park, 2021. "Correlation Analysis between Hydrologic Flow Metrics and Benthic Macroinvertebrates Index (BMI) in the Han River Basin, South Korea," Sustainability, MDPI, vol. 13(20), pages 1-17, October.

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