The XGBoost and the SVM-based prediction models for bioretention cell decontamination effect

Wang, Xiaocheng; Fu, Dafang; Wang, Yajun; Guo, Ying; Ding, Yong

doi:10.1007/s12517-021-07013-6

Cited by 10 publications

(2 citation statements)

References 51 publications

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“…The research of Mohsen (2021) [33] showed that SVM and XGBoost ranked first in predicting leak and nonleak samples in a laboratory-scale water distribution system. Moreover, Wang et al (2021) [34] found that XGBoost had a better generalization ability than SVM, as XGBoost could improve its prediction accuracy via the decontamination effect. Nagaraj and Lakshmi (2021) [35] reported that the XGBoost classifier outperformed the other machine learning algorithms assessed in their study in terms of water body extraction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply

Wang

Sun

et al. 2022

Sustainability

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Leakage occurs in rural water supply pipelines very often and its locating is quite demanding even for specialists, which could result in a poor operation efficiency of rural water supply projects and thus a low rural water supply guarantee rate. In view of this problem, the detection of leakage, as well as its prediction, is of great significance for the operation, maintenance, and administration of rural water supply projects. The traditional monitoring-warning systems for urban water distribution networks cannot be applied to rural water distribution networks, due to various limitations. Meanwhile, as with the traditional models, most new approaches based on machine learning such as the artificial neural network (ANN), probabilistic neural network (PNN), and statistical learning theory (SLT) do not fit rural water distribution networks much better, being unable to converge and force high-accuracy results with small sample sizes, which is a crucial demand to meet when dealing with rural water supply pipelines. Extreme gradient boosting (XGBoost), a model that specializes in small sample sizes and has a high generalization ability, was applied to a rural water supply project in Ningxia, China. In this study, a monitoring-warning system featuring both leakage locating and quantity estimation was established based on XGBoost. The accuracy and F1-score of the leakage locating model were 95% and 93%, respectively, while those of the leakage quantity model reached 96% and 97%, respectively. Furthermore, the pressure of monitoring stations could be obtained through the feature importance analysis enabled by XGBoost, which is essential for leakage warning. These results indicate that this system based on XGBoost could be a promising solution to the leakage issue in rural water supply projects, as a great inspiration for future developments in intelligent monitoring-warning systems, thus providing reliable approaches for the sustainable development of rural drinking water supply projects.

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Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply

Wang

Sun

et al. 2022

Sustainability

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“…XGBoost performs better than Support Vector Machines (SVM) in discriminating certain diseases in patients from healthy controls, using confusion matrix as its evaluation metric (Binson et al, 2021;Ogunleye et al, 2019). On the soil liquefaction prediction, whose data are sampled using different techniques, the study found that XGBoost perform better than Random Forests and SVM Demir et al (2022), data undergoing transformation Sahin (2023), better than random forest and gradient boosting machine on landslide data using RMSE as the evaluation metric Sahin (2020), better than logistic regression, Bayesian Additive Regression Tree (BART), random forest, and SVM on tumor classification problem Zhang et al (2023), better than SVM and K-nearest neighbour (KNN) on company bankruptcy classification problem Muslim et al (2021), and on surface water flooding data that stated XGBoost had a better generalization ability than SVM to improve prediction accuracy (Wang et al, 2021).…”

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confidence: 99%

Prediction of Maternity Recovery Rate of Group Long-Term Disability Insurance Using XGBoost

Kusnadi,

Wijaya,

Lesmono

2023

JTAM

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To help insurers determine insurance rates incorporating maternity factors, it is crucial to understand the maternity recovery rate, which was a metric used by insurance companies to understand how much of the expenses associated with maternity care and related medical services are covered by their policies. This paper employed Extreme Gradient Boosting (XGBoost), a powerful method for handling complex data relationships and preventing overfitting, on North American Group Long-Term Disability dataset obtained from the Society of Actuaries, which listed maternity as one of its categories, to predict the maternity recovery rate. In comparison, other machine learning methods such as Gradient Boosting Machine (GBM) and Bayesian Additive Regression Tree (BART) were used, with Root Mean Squared Error (RMSE) values calculated the difference between predicted and observed maternity recovery rates. Four datasets, 3 imbalanced and 1 fairly-balanced, were created out of the original dataset to test each method’s predictive prowess. The study revealed that XGBoost performed exceptionally well on the imbalanced datasets, while BART showed slight superiority in fairly-balanced data. Furthermore, the model identified the duration, exposures, and age of participants in both predicting maternity recovery rates and the underwriting process.

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Utilizing machine learning models to grasp water quality dynamic changes in lake eutrophication through phytoplankton parameters

Fang,

Huang,

Zhang

et al. 2024

Front. Environ. Sci. Eng.

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The XGBoost and the SVM-based prediction models for bioretention cell decontamination effect

Cited by 10 publications

References 51 publications

A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply

A Novel Intelligent Leakage Monitoring-Warning System for Sustainable Rural Drinking Water Supply

Prediction of Maternity Recovery Rate of Group Long-Term Disability Insurance Using XGBoost

Utilizing machine learning models to grasp water quality dynamic changes in lake eutrophication through phytoplankton parameters

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