Effect of Multicollinearity on the Bivariate Frequency Analysis of Annual Maximum Rainfall Events

Yoo, Chulsang; Cho, Eunsaem

doi:10.3390/w11050905

Cited by 12 publications

(8 citation statements)

References 57 publications

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“…Machine learning modelling accuracy can be impacted by multi-collinearity problems if a high correlation exists between feature variables [ 45 ]. It has been found that light absorbance for a specific wavelength is highly correlated to the neighbouring wavelengths, and correlation gradually decreases to far wavelengths.…”

Section: Methodsmentioning

confidence: 99%

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

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The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

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Section: Methodsmentioning

confidence: 99%

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

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“…Multi-collinearity arises when inter-correlation between input features is strong [27,28]. It can be a problem in statistical analysis such as regression as it distorts the prediction results of the model [27,28].…”

Section: Multi-collinearitymentioning

confidence: 99%

“…Nonetheless, feature selection that is not derived from the physical or biological processes related to target variables may give poor performances to the models [16,25,26]. Moreover, pre-selection of features without considering the statistical characteristics such as multi-collinearity can be an obstacle to developing a robust model [27,28].…”

Section: Introductionmentioning

confidence: 99%

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Kim

Jonoski

Solomatine

2022

Water

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Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on cyanobacterial cell density after a week, which was used as the target variable. We developed 96 machine learning models for Chilgok weir using four classification algorithms: k-Nearest Neighbor, Decision Tree, Logistic Regression, and Support Vector Machine. In the modeling methodology, we first selected input features by applying ANOVA (Analysis of Variance) and solving a multi-collinearity problem as a process of feature selection, which is a method of removing irrelevant features to a target variable. Next, we adopted an oversampling method to resolve the problem of having an imbalanced dataset. Consequently, the best performance was achieved for models using datasets divided into two classes, with an accuracy of 80% or more. Comparatively, we confirmed low accuracy of approximately 60% for models using datasets divided into three classes. Moreover, while we produced models with overall high accuracy when using logCyano (logarithm of cyanobacterial cell density) as a feature, several models in combination with air temperature and NO3-N (nitrate nitrogen) using two classes also demonstrated more than 80% accuracy. It can be concluded that it is possible to develop very accurate classification-based machine learning models with two features related to cyanobacterial blooms. This proved that we could make efficient and effective models with a low number of inputs.

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“…But majority of the studies in transportation engineering that uses multiple linear regression did not pay much attention to this multicollinearity problem. Studies in others fields like water resources engineering [19,20], electrical engineering [21] and tourism management [22] reported various methods like ridge regression, partial least squares regression and principal component regression to overcome the multicollinearity problem. In this study, we proposed a simple method of defining new variables as a linear combination and ratio of existing independent variables, which not only remove the multicollinearity problem but also help to achieve high R 2 value and significant p values.…”

Section: Introductionmentioning

confidence: 99%

Dealing with Multicollinearity Problem in Analysis of Side Friction Characteristics Under Urban Heterogeneous Traffic Conditions

Singh

Kumar

2021

Arab J Sci Eng

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Disturbance to the moving traffic, such as on-street parking and bus stop on carriageway normally called as side friction in the literature, is one of the major problems that impact the vehicular speed on urban arterials in developing countries like India. Regression model to study the mode-wise vehicular speeds using side friction and other influencing variables faces the problem of multicollinearity as there is a strong correlation between the independent variables which results in high R2 and high p values. Instead of dropping the highly correlated variables which is the conventional practice to deal with multicollinearity, we proposed an approach of taking a linear combination and ratio of independent variables. To test the applicability of the proposed approach, traffic data from five locations in Hyderabad and Vellore in India having a wide variety of geometric and traffic characteristics with one or more side frictions were collected and regression models were built. It was found that the proposed approach of taking linear combination and ratio worked very well as the models exhibited a high R2, low p values and low variance inflation factor. The regression models also forecast speeds with good and reasonable accuracy. For better understanding of the impact of side friction, the present study proposed an index called side friction index and it was found that the number of side friction elements and their impact are directly proportional. Also, the impact is not uniform across the different vehicle types. Though the present study focussed on side friction problem, the approach that we proposed in this paper to deal with multicollinearity can be applied in other fields as well.

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Effect of Multicollinearity on the Bivariate Frequency Analysis of Annual Maximum Rainfall Events

Cited by 12 publications

References 57 publications

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Dealing with Multicollinearity Problem in Analysis of Side Friction Characteristics Under Urban Heterogeneous Traffic Conditions

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