A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

Gai, Rongli; Guo, Zhifang

doi:10.3389/fpls.2023.1099668

Cited by 16 publications

(7 citation statements)

References 25 publications

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“…GRA is a data analysis method based on grey system theory that aims to study the interrelatedness between multiple indicators [20]. The data associated with each indicator are summed to determine the relative degree of each indicator, and the grey correlation between indicators is calculated to determine the degree of influence of each indicator on a problem.…”

Section: Methodology For the Selection Of Characteristic Indicatorsmentioning

confidence: 99%

Construction Project Cost Prediction Method Based on Improved BiLSTM

Wang,

Qiao

2024

Applied Sciences

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In construction project management, accurate cost forecasting is critical for ensuring informed decision making. In this article, a construction cost prediction method based on an improved bidirectional long- and short-term memory (BiLSTM) network is proposed to address the high interactivity among construction cost data and difficulty in feature extraction. Firstly, the correlation between cost-influencing factors and the unilateral cost is calculated via grey correlation analysis to select the characteristic index. Secondly, a BiLSTM network is used to capture the temporal interactions in the cost data at a deep level, and the hybrid attention mechanism is incorporated to enhance the model’s feature extraction capability to comprehensively capture the interactions among the features in the cost data. Finally, a hyperparameter optimisation method based on the improved particle swarm optimisation algorithm is proposed using the prediction accuracy as the fitness function of the algorithm. The MAE, RMSE, MPE, MAPE, and coefficient of determination of the simulated prediction results of the proposed method on the dataset are 7.487, 8.936, 0.236, 0.393, and 0.996%, respectively, where MPE is a positive coefficient. This avoids the serious consequences of underestimating the cost. Compared with the unimproved BiLSTM, the MAE, RMSE, and MAPE are reduced by 15.271, 18.193, and 0.784%, respectively, which reflects the superiority and effectiveness of the method and can provide technical support for project cost estimation in the construction field.

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Section: Methodology For the Selection Of Characteristic Indicatorsmentioning

confidence: 99%

Construction Project Cost Prediction Method Based on Improved BiLSTM

Wang,

Qiao

2024

Applied Sciences

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“…In addition to this, Li J [ 23 ] proposed a multi-classification algorithm based on a dual support vector machine decision tree to address security and privacy issues in IoT data. Gai R et al [ 24 ] presented a river water quality assessment method using an improved grey relational analysis (ACGRA) and particle swarm optimization multi-class support vector machine (PSO-MSVM). This method is applied to evaluate the environmental quality of river water.…”

Section: Related Workmentioning

confidence: 99%

Fabric tearing performance state perception and classification driven by multi-source data

Huang,

Jiao,

Zhang

et al. 2024

PLoS ONE

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The tear strength of textiles is a crucial characteristic of product quality. However, during the laboratory testing of this indicator, factors such as equipment operation, human intervention, and test environment can significantly influence the results. Currently, there is a lack of traceable records for the influencing factors during the testing process, and effective classification of testing activities is not achieved. Therefore, this study proposes a state-awareness and classification approach for fabric tear performance testing based on multi-source data. A systematic design is employed for fabric tear performance testing activities, which can real-time monitor electrical parameters, operational environment, and operator behavior. The data are collected, preprocessed, and a Decision Tree Support Vector Machine (DTSVM) is utilized for classifying various working states, and introducing ten-fold cross-validation to enhance the performance of the classifier, forming a comprehensive awareness of the testing activities. Experimental results demonstrate that the system effectively perceives fabric tear performance testing processes, exhibiting high accuracy in the classification of different fabric testing states, surpassing 98.73%. The widespread application of this system contributes to continuous improvement in the workflow and traceability of fabric tear performance testing processes.

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“…These principal components are linear combinations of the original variables and are ordered by the amount of variance they explain. Mathematically, PCA seeks to find the eigenvalues and eigenvectors of the covariance matrix of the original data, or equivalently, of the correlation matrix when the data are standardized [14][15][16] .…”

Section: Key Indicator Extraction Via Principal Component Analysismentioning

confidence: 99%

Integrative Performance Assessment of Electrical Distribution Networks: An Integrated Indicators and Weight Processing Strategy

Hu,

Chai,

Jing

et al. 2024

Preprint

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This study presents a novel framework This study proposes a new framework for evaluating the performance of distribution networks using a comprehensive approach. This method combines Principal Component Analysis (PCA) for extracting key performance indicators, Improved Analytic Hierarchy Process (IAHP) for determining objective weights of these indicators, and CRITIC method for determining subjective weights. Then, aggregate weights are used to calculate the comprehensive performance score of each part of the distribution network. On the premise of reducing computing resources, K-means clustering analysis is applied to classify the status of distribution network projects, providing data reference for project maintenance and repair. Finally, a case study and corresponding analysis of the evaluation results were presented, providing detailed insights into the practical application of the proposed framework. The limitations of current research and future research approaches were also discussed. The results indicate that the designed strategy performs well in terms of accuracy and computational convenience, and has potential application prospects

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A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

Cited by 16 publications

References 25 publications

Construction Project Cost Prediction Method Based on Improved BiLSTM

Construction Project Cost Prediction Method Based on Improved BiLSTM

Fabric tearing performance state perception and classification driven by multi-source data

Integrative Performance Assessment of Electrical Distribution Networks: An Integrated Indicators and Weight Processing Strategy

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