Integrative neural networks models for stream assessment in restoration projects

The ANN models were trained on the randomly selected 3/4 of the dataset of 112 streams in Ontario, Canada and validated on the remaining 1/4. The R2 values for the developed ANN model predictions were 0.86 for HBI and 0.92 for Richness. Sensitivity analysis of the trained ANN models revealed that Richness was directly proportional to Erosion and Riparian Width and inversely proportional to Floodplain Quality and Substrate parameters. HBI was directly proportional to Velocity Types and Erosion and inversely proportional to Substrate, % Treed and 1:2 Year Flood Flow parameters. The ANN models can be useful tools for watershed managers in stream assessment and restoration projects by allowing consideration of watershed properties in the stream assessment.