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

Abstract

Abstract The severity of ill effects (SEV) index is based on the limited meta-analysis of previous peer reviewed reports and consultations, and described as a function of duration of exposure to turbid conditions in fisheries or fish life stages by fish adapted to life in clear water ecosystems. In this study, the performance of classification by SEV index was investigated using the K-Means clustering algorithm. This study is based on 303 tests undertaken on aquatic ecosystem quality over a wide range of sediment concentrations (1–50,000 mg SS/L) and durations of exposure (1–35,000 h). Training and testing data includes concentration of suspended sediment, duration of exposure, species and life stages as the input variables and the SEV index for fish as the output variable. Results indicate 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.