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Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

Author

Listed:
  • Kieu Anh Nguyen

    (Dept. of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Walter Chen

    (Dept. of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Bor-Shiun Lin

    (Disaster Prevention Technology Research Center, Sinotech Engineering Consultants, Taipei 11494, Taiwan)

  • Uma Seeboonruang

    (Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

Abstract

This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

Suggested Citation

  • Kieu Anh Nguyen & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang, 2020. "Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:2022-:d:329429
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    References listed on IDEAS

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    Cited by:

    1. Ieva Meidute-Kavaliauskiene & Milad Alizadeh Jabehdar & Vida Davidavičienė & Mohammad Ali Ghorbani & Saad Sh. Sammen, 2021. "A Simple Way to Increase the Prediction Accuracy of Hydrological Processes Using an Artificial Intelligence Model," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    2. Kent Thomas & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang, 2020. "Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed," Sustainability, MDPI, vol. 12(15), pages 1-21, August.
    3. Jinxi Su & Rong Tang & Huilong Lin, 2024. "Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method," Land, MDPI, vol. 13(9), pages 1-20, September.

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