Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data
Auteur(s): |
Jiake Fu
Huijing Tian Lingguang Song Mingchao Li Shuo Bai Qiubing Ren |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Engineering, Construction and Architectural Management, 2021, n. 7, v. 28 |
Page(s): | 2023-2041 |
DOI: | 10.1108/ecam-05-2020-0357 |
Abstrait: |
PurposeThis paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data. Design/methodology/approachThe paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing. FindingsThe paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R²) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model. Originality/valueMachine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R²) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation. |
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10577109 - Publié(e) le:
26.02.2021 - Modifié(e) le:
02.09.2021