Evaluation of Classification Algorithms to Predict Largemouth Bass (Micropterus salmoides) Occurrence

Zhonghyun Kim,Kwang-Guk An,Taeyong Shim,Seo Jin Ki,Dongil Seo,Jinho Jung

doi:10.3390/su13179507

Abstract

This study aimed to evaluate classification algorithms to predict largemouth bass (Micropterus salmoides) occurrence in South Korea. Fish monitoring and environmental data (temperature, precipitation, flow rate, water quality, elevation, and slope) were collected from 581 locations throughout four major river basins for 5 years (2011–2015). Initially, 13 classification models built in the caret package were evaluated for predicting largemouth bass occurrence. Based on the accuracy (>0.8) and kappa (>0.5) criteria, the top three classification algorithms (i.e., random forest (rf), C5.0, and conditional inference random forest) were selected to develop ensemble models. However, combining the best individual models did not work better than the best individual model (rf) at predicting the frequency of largemouth bass occurrence. Additionally, annual mean temperature (12.1 °C) and fall mean temperature (13.6 °C) were the most important environmental variables to discriminate the presence and absence of largemouth bass. The evaluation process proposed in this study will be useful to select a prediction model for the prediction of freshwater fish occurrence but will require further study to ensure ecological reliability.

Highlights

Previous studies have built ensemble models by integrating all the candidate algorithms into the model [1] or by weighting candidate algorithms based on their accuracy [20,36]
In ad- contributed to the prediction of largemouth bass occurrence, following conventional climatic (temperdition, water quality variables (TotalN, total phosphorus (TotalP), and total suspended solids (TotalSS)) substantially contributed to ature and precipitation)
Given that annual mean temperature and fall mean the prediction of largemouth bass occurrence, following conventional climatic variables

Summary

Introduction

Various classification algorithms have been used to predict the presence of freshwater fish under certain environmental conditions [1,2,3]. Boosted regression tree [4], classification tree [5], genetic algorithm for rule-set prediction [2,6], logistic regression [3], generalized additive model [7], and artificial neural networks [1,8] have been used for freshwater fish prediction. Fukuda et al analyzed the occurrence of the invasive fish Pseudorasbora parva using a random forest algorithm [9]. Kwon et al used a random forest model among six candidate models and predicted the occurrence of 22 endemic fishes in South Korea [11]

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Conclusion