Condition-index based new ridge regression estimator for linear regression model with multicollinearity

Dar, Irum Sajjad; Chand, Sohail; Shabbir, Maha; Kibria, B. M. Golam

doi:10.1016/j.kjs.2023.02.013

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…Within the domain of scale-related variables, operational conditions and influent characteristics were interconnected because of biochemical treatment mechanisms . During the spatial modeling, ridge regression’s ability to address multicollinearity issues effectively suppressed the adverse effects of input features on predictions …”

Section: Resultsmentioning

confidence: 99%

“…45 During the spatial modeling, ridge regression's ability to address multicollinearity issues effectively suppressed the adverse effects of input features on predictions. 46 Conversely, for temporal modeling, the situation highly differed. In this case, scale-related variables had a more normal impact, while quality-related variables carried greater weight compared to the spatial data set.…”

Section: Spatial and Temporal Modeling 321 Comparison On Combinations...mentioning

confidence: 99%

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Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

Huang,

Yu,

Wang

et al. 2023

ACS EST Water

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To explore the water-energy-carbon nexus of wastewater treatment (WWT), advanced tools such as machine learning play a crucial role. Current research has primarily constructed energy efficiency models, but there exists a lack in considering comprehensive dimensions and comparing pollutant removal types. In this study, we conducted spatial and temporal modeling to predict the energy consumption (EC) of WWT via machine learning approaches. EC (kWh) was the target feature, with the input features covering operational conditions, environmental benefits, and externalities. The optimal spatial model obtained a test R 2 of 0.8224 in ridge regression, while the temporal model achieved a test R 2 of 0.7253 in random forest. Besides, the removal amount (10 3 kg) fit best with EC during the spatial modeling, while the discharge concentration (mg/L) fit best with EC during the temporal modeling. Notably, treatment volume, the removal of chemical oxygen demand, and the removal of ammonia nitrogen emerged as the most significant factors. Given this, our findings suggest optimization implications including scale economy utilization and aeration improvement. The spatial and temporal dimensions also illuminated tailored strategies on influent regulation, technology selection, and effluent standard settings for a specific region and season. Results will provide valuable guidance for existing operation and future design of WWT projects toward energy-saving and carbon neutrality.

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Section: Resultsmentioning

confidence: 99%

Section: Spatial and Temporal Modeling 321 Comparison On Combinations...mentioning

confidence: 99%

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

Huang,

Yu,

Wang

et al. 2023

ACS EST Water

View full text Add to dashboard Cite

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“…By driving some coefficients to zero, LASSO regression can effectively identify the most important predictors and discard irrelevant ones. On the other hand, Ridge regression is applied to estimate regression parameters while avoiding multicollinearity among independent variables, 50 expressed as:where ∝ is the regularization weight.…”

Section: Methodsmentioning

confidence: 99%

An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

Faridah,

Utami,

Wijaya

et al. 2024

Building Services Engineering Research and Technology

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Indoor air quality is the foundation of a good indoor environment. The COVID-19 pandemic further highlighted the importance of providing real-time airflow distribution information within the Building Environmental Monitoring System (BEMS) to minimize the risk of infectious airborne transmission. This paper discusses the process of developing a predictive model for indoor airflow distribution prediction with indoor and outdoor input parameters using machine learning and its implementation in healthy BEMS for a classroom in the tropical climate region of Yogyakarta, Indonesia. This paper encompassed field measurement and simulation involving outdoor climate conditions and the operational status of the classroom’s windows, Air Conditioning units, and fans. Three machine learning models were constructed using OLS, LASSO, and Ridge methods. Datasets for the modeling were generated from CFD model simulations in IES VE and were assessed for correlation. The mean temperature and velocity differences between the CFD model simulation and measurement results are 0.21°C and 0.083 m/s, respectively. Outdoor climate conditions and the operational status of the classroom’s utilities significantly influence the indoor airflow distribution characteristics. The three models indicate a relatively poor performance, where the classroom had a relatively low sensitivity to input changes. However, the best model performance was achieved using the LASSO method, with average values from post-normalization of [Formula: see text] and Root Mean Square Error (RMSE) of 0.336 and 0.077, respectively. The model was implemented in healthy BEMS on the “Platform for Healthy and Energy Efficient Building Management System.” Practical Application: This research proposed a machine learning model of indoor airflow characteristics of a classroom in Yogyakarta. The proposed model can be adapted to produce monitoring systems that best represent the related conditions. The method can be adopted to develop a relatively simple, low-cost sensor or model to monitor an indoor environment. Future studies may explore the results of the real-world implementation in a case study.

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“…As λ increases, the intensity of regularization increases and the complexity of the model decreases. Through cross-validation, we can find the best λ value that minimizes the cross-validation error [23][24][25][26]. In this paper, we use five-fold cross-validation to get the best λ value under different datasets, as shown in Table 8.…”

Section: Modelingmentioning

confidence: 99%

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

Li,

An,

Cheng

et al. 2024

Applied Sciences

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To improve the noise comfort of the whole machine, it is necessary to establish the sound quality prediction model of the Hy-Vo chain transmission system. Compared with the silent chain transmission system, the Hy-Vo chain transmission system normally operates at a lower speed and cannot have too much load at the limit speed. It is difficult to obtain a sufficient quantity of high-quality noise samples because there are few different working conditions. For small sample sound quality prediction, we use a sample enhancement method called fuzzy generation based on fuzzy mathematics. Firstly, audio samples of the Hy-Vo chain transmission system are collected through noise tests. Secondly, the processed samples are evaluated objectively and subjectively. After a correlation test of the subjective evaluation results, correct subjective evaluation scores of each noise sample are obtained. With the help of fuzzy generation, we can obtain a sufficient number of new samples. By mixing the original samples with the generated samples, a new dataset is created. Through using a general regression neural network (GRNN), support vector regression (SVR) model, and ridge regression (RR) method, the sound quality of the Hy-Vo chain transmission system can be predicted. Different from prediction results under the original dataset, using the fuzzy generation method can not only significantly reduce the prediction error of the model but also improve stability.

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Condition-index based new ridge regression estimator for linear regression model with multicollinearity

Cited by 14 publications

References 13 publications

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

Spatial and Temporal Modeling on Energy Consumption of Wastewater Treatment Based on Machine Learning Algorithms

An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

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