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

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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.

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Funding

This work was supported by the Ferdowsi University of Mashhad (grant number FUM-64635).

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Authors and Affiliations

  1. Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran

    Haniyeh Asadi & Mohammad T. Dastorani

  2. Mountain Societies Research Institute, University of Central Asia, Bishkek, Kyrgyzstan

    Roy C. Sidle

  3. Department of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

    Afshin Jahanshahi

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  1. Haniyeh Asadi
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  2. Mohammad T. Dastorani
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  3. Roy C. Sidle
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  4. Afshin Jahanshahi
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Contributions

Haniyeh Asadi: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft. Mohammad T. Dastorani: Methodology, Supervision, Writing – review & editing. Roy C. Sidle: Methodology, Writing – review & editing. Afshin Jahanshahi: Formal analysis, Writing – review & editing.

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Correspondence to Mohammad T. Dastorani.

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Asadi, H., Dastorani, M.T., Sidle, R.C. et al. A Comparative Assessment of Decision Tree Algorithms for Index of Sediment Connectivity Modelling. Water Resour Manage 38, 2293–2313 (2024). https://doi.org/10.1007/s11269-024-03760-9

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