• Title/Summary/Keyword: Tree-based pipeline optimization (TPOT)

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A Study on Time Series Cross-Validation Techniques for Enhancing the Accuracy of Reservoir Water Level Prediction Using Automated Machine Learning TPOT (자동기계학습 TPOT 기반 저수위 예측 정확도 향상을 위한 시계열 교차검증 기법 연구)

  • Bae, Joo-Hyun;Park, Woon-Ji;Lee, Seoro;Park, Tae-Seon;Park, Sang-Bin;Kim, Jonggun;Lim, Kyoung-Jae
    • Journal of The Korean Society of Agricultural Engineers
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    • v.66 no.1
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    • pp.1-13
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    • 2024
  • This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R2 (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

WQI Class Prediction of Sihwa Lake Using Machine Learning-Based Models (기계학습 기반 모델을 활용한 시화호의 수질평가지수 등급 예측)

  • KIM, SOO BIN;LEE, JAE SEONG;KIM, KYUNG TAE
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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    • v.27 no.2
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    • pp.71-86
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    • 2022
  • The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DOBot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DINSur) and dissolved inorganic phosphorus (DIPSur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.