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A Comparative Assessment of Decision Tree Algorithms for Index of Sediment Connectivity Modelling

Author

Listed:
  • Haniyeh Asadi

    (Ferdowsi University of Mashhad)

  • Mohammad T. Dastorani

    (Ferdowsi University of Mashhad)

  • Roy C. Sidle

    (University of Central Asia)

  • Afshin Jahanshahi

    (Sari Agricultural Sciences and Natural Resources University)

Abstract

Assessment of the spatial distribution of potential pathways of sediment transport and the degree of linkage between sediment sources and the channel network within a watershed represents a valuable analysis for informing management decisions on sediment yield and transfer. Given the limitations of conventional methods for determining index of sediment connectivity (IC), there is a need to provide a flexible and efficient approach with the ability to apply different factors. In this regard, five decision tree-based machine learning models: M5 prime (M5P), random tree (RT), random forest (RF), alternating model tree (AMT), and reduced error pruning tree (REPT) were tested using geomorphic and climatic factors. Two databases were constructed with 200 and 1600 classes at 50 watersheds in Queensland, Australia. In these models, IC was assessed as an output parameter and six attributes that affect IC were assigned as input parameters (i.e., elevation, slope, area, length of stream channel, normalized difference vegetation index, and rainfall). Statistical validation and comparison of model predictions with calculated IC values based on the approach of Borselli et al. (Catena 75:268–277, 2008) were performed. Based on the statistical criteria, the RF model produced the most robust estimations of IC compared to other models and performed very well for IC modelling, especially in smaller subsections of watersheds. Accordingly, these findings can play an effective role for implementing watershed management and soil and water resources management measures.

Suggested Citation

  • Haniyeh Asadi & Mohammad T. Dastorani & Roy C. Sidle & Afshin Jahanshahi, 2024. "A Comparative Assessment of Decision Tree Algorithms for Index of Sediment Connectivity Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2293-2313, May.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:7:d:10.1007_s11269-024-03760-9
    DOI: 10.1007/s11269-024-03760-9
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    References listed on IDEAS

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    1. Min Yan Chia & Chai Hoon Koo & Yuk Feng Huang & Wei Chan & Jia Yin Pang, 2023. "Artificial Intelligence Generated Synthetic Datasets as the Remedy for Data Scarcity in Water Quality Index Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 6183-6198, December.
    2. Elham Fijani & Khabat Khosravi, 2023. "Hybrid Iterative and Tree-Based Machine Learning Algorithms for Lake Water Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(14), pages 5431-5457, November.
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