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http://dx.doi.org/10.15681/KSWE.2019.35.5.432

A Study on Predicting TDI(Trophic Diatom Index) in tributaries of Han river basin using Correlation-based Feature Selection technique and Random Forest algorithm  

Kim, Minkyu (ETWaters Inc.)
Yoon, Chun Gyeong (Department of Environmental Health Science, Konkuk University)
Rhee, Han-Pil (ETWaters Inc.)
Hwang, Soon-Jin (Department of Environmental Health Science, Konkuk University)
Lee, Sang-Woo (Department of Forestry and Landscape Architecture, Konkuk University)
Publication Information
Journal of Korean Society on Water Environment / v.35, no.5, 2019 , pp. 432-438 More about this Journal
Abstract
The purpose of this study is to predict Trophic Diatom Index (TDI) in tributaries of the Han River watershed using the random forest algorithm. The one year (2017) and supplied aquatic ecology health data were used. The data includes water quality(BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, water temperature, DO, pH, conductivity, turbidity), hydraulic factors(water width, average water depth, average velocity of water), and TDI score. Seven factors including water temperature, BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, and average water depth are selected by the Correlation Feature Selection. A TDI prediction model was generated by random forest using the seven factors. To evaluate this model, 2017 data set was used first. As a result of the evaluation, $R^2$, % Difference, NSE(Nash-Sutcliffe Efficiency), RMSE(Root Mean Square Error) and accuracy rate show that this model is compatible with predicting TDI. To be more concrete, $R^2$ is 0.93, % Difference is -0.37, NSE is 0.89, RMSE is 8.22 and accuracy rate is 70.4%. Also, additional evaluation using data set more than 17 times the measured point was performed. The results were similar when the 2017 data set were used. The Wilcoxon Signed Ranks Test shows there was no statistically significant difference between actual and predicted data for the 2017 data set. These results can specify the elements which probably affect aquatic ecology health. Also, these will provide direction relative to water quality management for a watershed that must be continuously preserved.
Keywords
Aquatic ecology health; Correlation-based Feature Selection; Random forest; TDI Prediction;
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Times Cited By KSCI : 5  (Citation Analysis)
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