A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture

Jamei, Mehdi; Ali, Mumtaz; Karbasi, Masoud; Sharma, Ekta; Jamei, Mozhdeh; Chu, Xuefeng; Yaseen, Zaher Mundher

doi:10.1016/j.engappai.2023.105895

Cited by 16 publications

(2 citation statements)

References 62 publications

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“…To solve the unconstrained variational problem after this transformation, the Alternate Direction Method of Multipliers (ADMM) method is used to implement alternate updates [61]. For the algorithm process, please refer to [62].…”

Section: Signal Noise Reduction and Reconstructionmentioning

confidence: 99%

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Mao,

Zhou,

et al. 2024

Front. Phys.

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To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

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Section: Signal Noise Reduction and Reconstructionmentioning

confidence: 99%

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Mao,

Zhou,

et al. 2024

Front. Phys.

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“…The model achieved relatively low error and high performance in predicting weekly soil moisture [116]. Utilizing the Soil Moisture Active Passive satellite dataset [117], Jamei et al devised a feature selection method that combines Boruta-GBDT with multivariate variable pattern decomposition (MVMD) for predicting surface soil moisture [118]. The prediction accuracy experienced significant improvement, establishing the model as currently the best for soil moisture prediction.…”

Section: Soil Moisture Predictionmentioning

confidence: 99%

Research on Water Resource Modeling Based on Machine Learning Technologies

Liu,

Zhou,

Yang

et al. 2024

Water

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Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling.

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Short-term wind power forecasting using the hybrid model of multivariate variational mode decomposition (MVMD) and long short-term memory (LSTM) neural networks

Ghanbari,

Avar

2024

Electr Eng

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A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture

Cited by 16 publications

References 62 publications

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Research on Water Resource Modeling Based on Machine Learning Technologies

Short-term wind power forecasting using the hybrid model of multivariate variational mode decomposition (MVMD) and long short-term memory (LSTM) neural networks

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