A novel feature susceptibility approach for a PEMFC control system based on an improved XGBoost-Boruta algorithm

Yuan, Xinjie; Chen, Fujun; Xia, Zenggang; Zhuang, Liying; Jiao, Kui; Peng, Zhijun; Wang, Bowen; Bucknall, Rwg; Yearwood, Konrad; Hou, Zhongjun

doi:10.1016/j.egyai.2023.100229

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…The major predictor variables are established by the distribution of Z-score metrics 29 . In this study, instead of a random forest, the XGBoost ensemble algorithm was used to calculate the Z-score 30 . The process flow of the Boruta algorithm can be summarized as follows: Random shadow characteristics are created.…”

Section: Methodsmentioning

confidence: 99%

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Karbasi,

Ali,

Bateni

et al. 2024

Sci Rep

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Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

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Section: Methodsmentioning

confidence: 99%

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Karbasi,

Ali,

Bateni

et al. 2024

Sci Rep

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show abstract

“…Extreme Gradient Boosting (XGBoost) is a tree-based integrated learning method, 34 which can realize regression and classification and is widely used in device fault diagnosis and prediction. 35–37 It has powerful advantages, such as fewer hyperparameters, 38 higher training performance of imbalanced data sets, 36 and fast calculation speed. 37 Wang et al 39 applied XGBoost to predict the fault of the fan bearing.…”

Section: Methodsmentioning

confidence: 99%

RSF-based model for predicting pump failure trends in tunnels

Wu,

Chen,

et al. 2023

Advances in Mechanical Engineering

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The water pump is a piece of crucial electromechanical equipment to ensure the safety of tunnels. Therefore, it’s essential to master the performance trend of pumps to prevent the occurrence of failure. In this paper, essential information and failure records of pumps in 15 operating tunnels for many years were collected. According to the data characteristics, a data-filling model based on XGBoost is developed to address the issue of the censored data. Considering that most pumps are still in operation, a failure prediction model based on Random Survival Forest (RSF) is designed by incorporating survival analysis principles. The proposed Pump Failure Trend Prediction Model (PFTPM) overcomes difficulties caused by the lack of previous data and the small number of old pumps. We identify two phases of failure: the first phase exhibits a bathtub-shaped failure rate curve, while the second phase is characterized by a lower failure risk. The importance of considering rainfall, pump operating time, and performance changes for effective maintenance planning is emphasized. Furthermore, we summarize the failure evolution law of various types of pumps to amend maintenance cycle in the existing specification. Overall, this paper integrates innovative big-data technologies into the traditional maintenance data of tunnel pumps.

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“…The major predictor variables are established by the distribution of Z-score metrics (Kursa et al 2010a). In this study, instead of a random forest, the XGBoost ensemble algorithm was used to calculate the Z-score (Yuan et al 2023). The process flow of the Boruta algorithm can be summarized as follows:…”

Section: Barratta Creekmentioning

confidence: 99%

Multistep Ahead Forecasting of Electrical Conductivity in Rivers by Using a Hybrid Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) Model Enhanced by Boruta-XGBoost Feature Selection Algorithm

Karbasi

Ali

Bateni

et al. 2023

Preprint

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Electrical conductivity (EC) is a key water quality metric for predicting the salinity and mineralization. In this study, the 10-day-ahead EC of two Australian rivers, Albert River and Barratta Creek, was forecasted using a novel deep learning algorithm, i.e., the convolutional neural network combined with long short-term memory (CNN-LSTM) model. The Boruta-extreme gradient boosting (XGBoost, XGB) feature selection method was used to determine the significant inputs (time series lagged data) for the model. The performance of the proposed Boruta-XGB-CNN-LSTM model was compared with those of three machine learning approaches: multi-layer perceptron neural network (MLP), K-nearest neighbor (KNN), and XGBoost, considering different statistical metrics such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error (MAPE). Ten years of data for both rivers were extracted, with data for seven (2012–2018) and three years (2019–2021) used for training and testing the models, respectively. The Boruta-XGB-CNN-LSTM algorithm outperformed the other models in forecasting the 1-day-ahead EC in both stations over the test dataset (R = 0.9429, RMSE = 45.6896, and MAPE = 5.9749 for Albert River; and R = 0.9215, RMSE = 43.8315, and MAPE = 7.6029 for Barratta Creek). In addition, the Boruta-XGB-CNN-LSTM model could effectively forecast the EC for the next 3–10 days. Nevertheless, the performance of the Boruta-XGB-CNN-LSTM model slightly deteriorated as the forecasting horizon increased from 3 to 10 days. Overall, the Boruta-XGB-CNN-LSTM model is an effective soft computing method for accurately predicting the EC fluctuation in rivers.

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A novel feature susceptibility approach for a PEMFC control system based on an improved XGBoost-Boruta algorithm

Cited by 16 publications

References 24 publications

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

RSF-based model for predicting pump failure trends in tunnels

Multistep Ahead Forecasting of Electrical Conductivity in Rivers by Using a Hybrid Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) Model Enhanced by Boruta-XGBoost Feature Selection Algorithm

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