Improving performance of classification on severity of ill effects (SEV) index on fish using K-Means clustering algorithm with various distance metrics

摘要

The severity of ill effects (SEV) index is based on the limited meta-analysis of previous peer reviewed reports andconsultations, and described as a function of duration of exposure to turbid conditions in fisheries or fish lifestages by fish adapted to life in clear water ecosystems. In this study, the performance of classification bySEV index was investigated using the K-Means clustering algorithm. This study is based on 303 tests undertakenon aquatic ecosystem quality over a wide range of sediment concentrations (1–50,000 mg SS/L) and durations ofexposure (1–35,000 h). Training and testing data includes concentration of suspended sediment, duration ofexposure, species and life stages as the input variables and the SEV index for fish as the output variable. Resultsindicate that the K-Means clustering algorithm, as an efficient novel approach with an acceptable range of error,can be used successfully for improving the performance of classification by SEV index.