Neural networks based linear (PCA) and nonlinear (ISOMAP) feature extraction for soil swelling pressure prediction (North East Algeria)

Ouassila, Bahloul; Zohra, Tebbi Fatima; Laid, Lekouara; Hizia, Bekhouche

doi:10.1016/j.heliyon.2023.e18673

Cited by 2 publications

(1 citation statement)

References 51 publications

(56 reference statements)

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“…(3) Result fusion: Principal component analysis (PCA) is known as a classic method for high-dimensional data preparation, especially in the field of explanatory data analysis and forecasting model conducting [53]. It specializes in data degradation, which not only preserves key information but also removes unanticipated noise [54].…”

Section: Feature Processingmentioning

confidence: 99%

Complex Real-Time Monitoring and Decision-Making Assistance System Based on Hybrid Forecasting Module and Social Network Analysis

Fan,

Li,

et al. 2024

Systems

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Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. Thus, this paper proposed a complex real-time monitoring and decision-making assistance system, using a hybrid forecasting module and social network analysis. Firstly, before an accurate forecasting module was constructed, text sentiment analysis and a strategy based on multiple feature selection methods and result fusion were introduced to data preprocessing. Subsequently, CNN-D-LSTM was proposed to improve the feature capture ability to make forecasting more accurate. Then, social network analysis was utilized to explore the spatial transporting characteristics, which could provide solutions to joint prevention and control in urban agglomerations. For experiment simulation, two comparative experiments were constructed for individual models and city cluster forecasting, in which the mean absolute error decreases to 7.8692 and the Pearson correlation coefficient is 0.9816. For overall spatial cluster forecasting, related experiments demonstrated that with appropriate cluster division, the Pearson correlation coefficient could be improved to nearly 0.99.

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Section: Feature Processingmentioning

confidence: 99%