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 sedi- ment yield and transfer. Given the limitations of conventional methods for determin- ing index of sediment connectivity (IC), there is a need to provide a flexible and effi- cient 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 for- est (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 dif- ference vegetation index, and rainfall). Statistical validation and comparison of model predictions with calculated IC values based on the approach of Borselli et al. (Cat- ena 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. Accord- ingly, these findings can play an effective role for implementing watershed manage- ment and soil and water resources management measures

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