UC Davis

Clustering and machine learning-based predictions are increasingly used for environmental data analysis and management. In fluvial geomorphology, examples include predicting channel types throughout a river network and segmenting river networks into a series of channel types, or groups of channel forms. However, when relevant information is unevenly distributed throughout a river network, the discrepancy between data-rich and data-poor locations creates an information gap. Combining clustering and predictions addresses this information gap, but challenges and limitations remain poorly documented. This is especially true when considering that predictions are often achieved with two approaches that are meaningfully different in terms of information processing: decision trees (e.g., RF: random forest) and deep learning (e.g., DNNs: deep neural networks). This presents challenges for downstream management decisions and when comparing clusters and predictions within or across study areas. To address this, we investigate the performance of RF and DNN with respect to the information gap between clustering data and prediction data. We use nine regional examples of clustering and predicting river channel types, stemming from a single clustering methodology applied in California, USA. Our results show that prediction performance decreases when the information gap between field-measured data and geospatial predictors increases. Furthermore, RF outperforms DNN, and their difference in performance decreases when the information gap between field-measured and geospatial data decreases. This suggests that mismatched scales between field-derived channel types and geospatial predictors hinder sequential information processing in DNN. Finally, our results highlight a sampling trade-off between uniformly capturing geomorphic variability and ensuring robust generalisation.