Research on predicting the productivity of cutter suction dredgers based on data mining with model stacked generalization

Wang, Bin; Fan, Shidong; Pan, Junjun; Xing, Ting; Fang, Zhenlong; Wen, Quan

doi:10.1016/j.oceaneng.2020.108001

Cited by 30 publications

(12 citation statements)

References 32 publications

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“…Pressure will influence the mud and water proportion that is pumped into pipeline. The variable S 165 shows a correlation of 0.34628, which is also mentioned by other researchers [5,27]. The flow rate may determine the mud sedimentation during pipeline transportation.…”

Section: Principal Components Analysis Based On Mechanism and Knowledgesupporting

confidence: 66%

“…Cutter suction dredgers are common and useful machines that can remove the mud deposited at the bottom of water and keep transportation routes in good condition [4]. Dredging productivity is one of the most important indexes to evaluate the dredging performance, which is affected by many factors such as soil properties, the power of the pump, the cutter structural parameters, and so on [5]. The process of sand being cut into a mixture of mud and water by a rotating cutter is very complicated.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

et al. 2021

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Dredging is a basic construction for waterway improvement, harbor basin maintenance, land reclamation, environmental protection dredging, and deep-sea mining. The dredging process of cutter suction dredgers is so complex that the operational data show strong characteristics of dynamic, nonlinearity, and time delay, which make it difficult to predict the productivity accurately via basic principles models. In this paper, we propose a novel integrating PCA-LSTM model to improve the productivity prediction of cutter suction dredger. Firstly, multiple variables are reduced in dimension and selected by PCA method based on the working mechanism of cutter suction dredger. Then the productivity is predicted via mud concentration in long short-term memory network with relevant operational time-series data. Finally, the proposed method is successfully applied to an actual case study in China. Also, it performs well in the cross-validation and comparative study for several important characteristics: (i) it involves the operational parameters based on the mechanism analysis; and (ii) it is a deep-learning-based approach that can deal with operation series data with a special memory mechanism. This study provides a heuristic idea for integrating the data-driven method and supervision of human knowledge for application in practical engineering.

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Section: Principal Components Analysis Based On Mechanism and Knowledgesupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

et al. 2021

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“…The RF provides an assessment of the importance of the different feature variables in the prediction process. To evaluate the importance of each feature (e.g., the satellite image band and vegetation index), the RF switches one of the input random variables while keeping the rest constant, and it measures the decrease in the accuracy using the OOB error estimation and the decrease in the Gini index [86,87]. In this study, we used the RF model to analyze the importance of the selected feature variables to the target value and ranked the importance of the features, which is presented in Figure 4.…”

Section: Reference Data Sampling and Preprocessingmentioning

confidence: 99%

“…Product inter-comparison: We used the model for data production, and compared and verified the produced data with the Global Forest Change 2000-2019 (GFC) datasets and GLS_TCC datasets. In this paper, the correlation coefficient (R 2 Equation (1)), mean absolute error (MAE, Equation ( 2)), and root mean square error (RMSE, Equation (3)) were selected as the evaluation indicators of the model's performance [42,87,109].…”

mentioning

confidence: 99%

Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

Liu

Liang

et al. 2021

Remote Sensing

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The accurate monitoring of forest cover and its changes are essential for environmental change research, but current satellite products for forest coverage carry many uncertainties. This study used 30-m Landsat-8 data, and aggregated 1-m GaoFen-2 (GF-2) satellite images to construct the training samples and used multiple machine learning algorithms (MLAs) to estimate the fractional forest cover (FFC) in China’s Three North Region (TNR). In this study, multiple MLAs were merged to construct stacked generalization (SG) models based on the idea of SG, and the performances of the MLAs in the FFC estimation were evaluated. The results of the 10-fold cross-validation showed that all non-linear algorithms had a good performance, with an R2 value of greater than 0.8 and a root-mean square error (RMSE) of less than 0.05. In the bagging ensemble, the random forest (RF) (R2 = 0.993, RMSE = 0.020) model performed the best and in the boosting ensemble, the light gradient boosted machine (LGBM) (R2 = 0.992, RMSE = 0.022) performed the best. Although the evaluation index of the RF is slightly better than that of the LGBM, the independent validation results show that the two models have similar performances. The model evaluation results of the independent datasets showed that, in the SG model, the performance of the SG(LGBM) (R2 = 0.991, RMSE = 0.034) was better than that of the single or non-ensemble model. Comparing the FFC estimates of our model with those of existing datasets showed that our model exhibited more forest spatial distribution details and higher accuracy in complex landscapes. Overall, in this study, the method of using high-resolution remote sensing (RS) images to extract samples for FFC estimation is feasible. Our results demonstrate the potential of the ensemble MLAs to map the FFC. The research results also show that among many MALs, the RF algorithm is the most suitable algorithm for estimating FFC, which provides a reference for future research.

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“…The mud density, water density, and soil density data were removed because the mud density was calculated by the mud concentration data in the actual construction of the dredger, so it cannot be used to predict the mud concentration [32]. The conversion relationship was as Equation (1).…”

Section: Data Cleaningmentioning

confidence: 99%

A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger

Wang

Fan

Pan

et al. 2020

Sensors

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The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit R2 values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration.

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Research on predicting the productivity of cutter suction dredgers based on data mining with model stacked generalization

Cited by 30 publications

References 32 publications

A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers

Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods

A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger

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