Performance comparison of eight models (S3). by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Predicted value of S1 station. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

S2 station noise reduction result. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Three station data sets. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

DO prediction framework. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Results of cross-validation and t-test. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Predicted value of S3 station. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

S3 station noise reduction result. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Data details. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Predicted value of S2 station. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Performance comparison of eight models (S2). by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Parameter settings. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

S1 station noise reduction result. by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Performance comparison of eight models (S1). by Yubo Zhao (3873457)

“…In this study, a hybrid machine learning model for river DO prediction, called DWT-KPCA-GWO-XGBoost, is proposed, which combines the discrete wavelet transform (DWT), kernel principal component analysis (KPCA), gray wolf optimization algorithm (GWO), and extreme gradient boosting (XGBoost). …”

Key parameters of DFIG. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

Parameter values. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

DC bus voltage variations. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

MPPT operating curve of the DFIG. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

Rotor-side three-phase current variation. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”

Structure of a DFIG. by Yanling Lv (327106)

“…Comparative simulations using the traditional NSGA-II, a multiobjective particle swarm optimization algorithm, and a multiobjective gray wolf optimization algorithm are conducted to validate the proposed algorithm. …”