[논문]The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods

Shin, Yuna; Lee, Heesuk; Lee, Young-Joo; Seo, Dae Keun; Jeong, Bomi; Hong, Seoksu; Kim, Jaehoon; Kim, Taekgeun; Lee, Jae-Kyeong; Heo, Tae-Young

doi:10.1155/2019/5749746

The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods 원문보기

Mathematical problems in engineering, v.2019, 2019년, pp.1 - 13

Shin, Yuna (Department of Water Environment Research, National Institute of Environmental Research, Incheon 22689, Republic of Korea) , Lee, Heesuk (K-water, Daejeon 34045, Republic of Korea) , Lee, Young-Joo (K-water, Daejeon 34045, Republic of Korea) , Seo, Dae Keun (K-water, Daejeon 34045, Republic of Korea) , Jeong, Bomi (Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Republic of Korea) , Hong, Seoksu (Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Republic of Korea) , Kim, Jaehoon (Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Republic of Korea) , Kim, Taekgeun (Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Republic of Korea) , Lee, Jae-Kyeong (Idea Commercialization Center, Korea Institute of Science Technology Information, Seoul 02456, Republic of Korea) , Heo, Tae-Young (Department of Information & Statistics, Chungbuk National University, Chungbuk 28644, Republic of Korea)

Abstract ▼ AI-Helper

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1-specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale-invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification-threshold-invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1-specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4-P, DO, BOD, NH3-N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.

참고문헌 (17)

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The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods 원문보기

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The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods 원문보기

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