The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods
| Autor: | Heesuk Lee, Seoksu Hong, Young-Joo Lee, Bomi Jeong, Yuna Shin, Jae-Kyeong Lee, Taekgeun Kim, Tae-Young Heo, Jaehoon Kim, Dae Keun Seo |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Boosting (machine learning)
010504 meteorology & atmospheric sciences Article Subject business.industry lcsh:Mathematics General Mathematics General Engineering Feature selection 010501 environmental sciences lcsh:QA1-939 Machine learning computer.software_genre Logistic regression 01 natural sciences Support vector machine lcsh:TA1-2040 Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business computer 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Mathematical Problems in Engineering, Vol 2019 (2019) |
| ISSN: | 1024-123X |
| DOI: | 10.1155/2019/5749746 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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