Extreme learning machine model for assessment of stream health using the habitat evaluation index

Extreme Learning Machine (ELM) approach is used to predict stream health with Qualitative Habitat Evaluation Index (QHEI), and watershed metrics. A dataset of 112 sites in Ontario, Canada with their Hilsenhoff Biotic Index (HBI) and richness values is used in two ELM models development. Each model used 70 and 30% of the dataset for training and testing respectively. The models show a great fit with Root Mean Square Error (RMSE)=0.12 and 0.33 for HBI and richness test models. Then, features elimination based on ELM coefficients and coefficient of variation show a slight increase in models' RMSE to reach 0.09 and 0.33 correspondingly. Accordingly, this high models' predictabilities of this research provide better insights on which factors influence HBI or Richness and that ELM has a better architecture than other machine learning models and ANN to learn the complex non-linear relationships. Also, sensitivity analysis expressed channel slope as the most affecting stream-health parameter for stream health.