Assessing and solving multicollinearity in sediment transport prediction models using principal component analysis

Sulaiman, Mohd Sofiyan; Abood, Manal Mohsen; Sinnakaudan, Shanker Kumar; Shukor, Mohd Rizal; You, Goh Qiu; Chung, Xiang Zhi

doi:10.1080/09715010.2019.1653799

Cited by 36 publications

(16 citation statements)

References 28 publications

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“…According to [70], multicollinearity occurs when the absolute value of the Pearson correlation coefficient is higher than 0.7. When multicollinearity occurs, this study performs a PCA [71], which is an unsupervised learning method to reduce the dimensionality of the dataset and avoid multicollinearity through eigenvalue decomposition. The PCA aims to extract the main variables by evaluating the significance of the variables on the mechanical properties.…”

Section: Multicollinearity and Principal Component Analysismentioning

confidence: 99%

Predicting Mechanical Properties of High-Performance Fiber-Reinforced Cementitious Composites by Integrating Micromechanics and Machine Learning

Guo

Meng

et al. 2021

Materials

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Current development of high-performance fiber-reinforced cementitious composites (HPFRCC) mainly relies on intensive experiments. The main purpose of this study is to develop a machine learning method for effective and efficient discovery and development of HPFRCC. Specifically, this research develops machine learning models to predict the mechanical properties of HPFRCC through innovative incorporation of micromechanics, aiming to increase the prediction accuracy and generalization performance by enriching and improving the datasets through data cleaning, principal component analysis (PCA), and K-fold cross-validation. This study considers a total of 14 different mix design variables and predicts the ductility of HPFRCC for the first time, in addition to the compressive and tensile strengths. Different types of machine learning methods are investigated and compared, including artificial neural network (ANN), support vector regression (SVR), classification and regression tree (CART), and extreme gradient boosting tree (XGBoost). The results show that the developed machine learning models can reasonably predict the concerned mechanical properties and can be applied to perform parametric studies for the effects of different mix design variables on the mechanical properties. This study is expected to greatly promote efficient discovery and development of HPFRCC.

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Section: Multicollinearity and Principal Component Analysismentioning

confidence: 99%

Predicting Mechanical Properties of High-Performance Fiber-Reinforced Cementitious Composites by Integrating Micromechanics and Machine Learning

Guo

Meng

et al. 2021

Materials

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“…The discrepancy ratio test was used to validate the estimated flows. The discrepancy ratio between the actual flows and estimated flows should fall within the ratio of 0.5 -2.0 to justify the reliability of proposed rating curve formula in equation 1 (Sinnakaudan et al, 2010;Sulaiman et al, 2019). The ratio of 1 depicts the unity between the measured flows and estimated flows.…”

Section: Software and Datasetsmentioning

confidence: 90%

An Assessment of a River Hydrological Regime Alteration: A Case Study of Sungai Dungun

ZAMRI¹,

Sulaiman²,

JUHAR³

et al. 2021

UMT JUR

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“…Clustering can be considered a more suitable method for representing multidimensional phenomena in time-space as it does not require the definition of cut-off levels or the normalization of indicators. Typically, clustering is employed in time (Franklin et al, 2020;Libório et al, 2018;Sulaiman et al, 2019) or space analyses (Alabi et al, 2019;Li & Wang, 2013;Von Landesberger et al, 2015). However, studies using clustering in time-space analyses are not rare (Apparicio et al, 2015;Delmelle, 2015;Foote & Walter, 2017;Foote, 2017;Ling & Delmelle, 2016;Liu et al, 2018;Nilsson & Delmelle, 2018).…”

Section: Which Methods Is Most Appropriate For This Type Of Problem?mentioning

confidence: 99%

Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

et al. 2021

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Composite Indicators are one-dimensional measurements that simplify the interpretation of multidimensional phenomena that facilitate public policies' elaboration. The literature on composite indicators is abundant, diversified, and inserted in practically all knowledge areas. Part of this literature aims to reduce uncertainties that propagate through the structure of the composite indicator during the process of normalization, weighting, and aggregation of indicators. Even if no composite indicator is exempt from criticism, the current literature is already sufficiently large and deep to guide researchers in constructing reliable composite indicators. However, most related works are concerned with representing multidimensional phenomena in time or space. Although some studies are interested in representing multidimensional phenomena that co-occur in time-space, the portion of the literature that addresses composite indicators is still not comprehensive, therefore leaving several open questions: What are the additional challenges in representing multidimensional phenomena in time-space? What methods can be used? Which method is most appropriate for this type of representation? What are the shortcomings of this method? How to reduce these shortcomings? This research aims at answering these questions in order to advance the time-space analysis of multidimensional phenomena. As a general contribution, the work presents a scheme of procedures that reduce subjectivities and uncertainties in the representations of multidimensional phenomena in time-space. As a specific contribution, it provides accurate and reliable information on the trajectory of social exclusion in the analyzed region. KeywordsComposite indicators • Multidimensional phenomena • Cluster analysis • Principal component analysis • Time-in-space analysis • Urban geography

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Assessing and solving multicollinearity in sediment transport prediction models using principal component analysis

Cited by 36 publications

References 28 publications

Predicting Mechanical Properties of High-Performance Fiber-Reinforced Cementitious Composites by Integrating Micromechanics and Machine Learning

Predicting Mechanical Properties of High-Performance Fiber-Reinforced Cementitious Composites by Integrating Micromechanics and Machine Learning

An Assessment of a River Hydrological Regime Alteration: A Case Study of Sungai Dungun

Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

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