A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

Chen, Jie; Hoogh, Kees de; Gulliver, John; Hoffmann, Barbara; Hertel, Ole; Ketzel, Matthias; Bauwelinck, Mariska; Donkelaar, Aaron van; Hvidtfeldt, Ulla Arthur; Katsouyanni, Klea; Janssen, Nicole; Martin, Randall V.; Samoli, Evangelia; Schwartz, Per E.; Stafoggia, Massimo; Bellander, Tom; Strak, Maciek; Wolf, Kathrin; Vienneau, Danielle; Vermeulen, Roel; Brunekreef, Bert; Hoek, Gerard

doi:10.1016/j.envint.2019.104934

Cited by 250 publications

(144 citation statements)

References 46 publications

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“…Many papers compared linear regression with random forest algorithms in other elds. As we can see, the random forest algorithm has not performed better in all eld and aspects (26)(27)(28), this reveals that random forest method can only take advantage over linear regression in some data models. So a more signi cant number of multi-centre data are needed to validate our outcomes in the eld of GFR estimation.…”

Section: Discussionmentioning

confidence: 93%

Applying Random Forest Model Algorithm to GFR estimation

Yang

Chong

et al. 2020

Preprint

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Background The utilization of estimating-GFR equations is critical for kidney disease in the clinic. However, the performance of the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation has not improved substantially in the past eight years. Here we hypothesized that random forest regression(RF) method could go beyond revised linear regression, which is used to build the CKD-EPI equation Methods 1732 participants were enrolled in this study totally (1333 in development data set from Tianhe District and 399 in external data set Luogang District). Recursive feature elimination (RFE) is applied to the development data to select important variables and build random forest models. Then same variables were used to develop the estimated GFR equation with linear regression as a comparison. The performances of these equations are measured by bias, 30% accuracy, precision and root mean square error(RMSE). Results Of all the variables, creatinine, cystatin C, weight, body mass index (BMI), age, uric acid(UA), blood urea nitrogen(BUN), hematocrit(HCT) and apolipoprotein B(APOB) were selected by RFE method. The results revealed that the overall performance of random forest regression models ascended the revised regression models based on the same variables. In the 9-variable model, RF model was better than revised linear regression in term of bias, precision ,30%accuracy and RMSE(0.78 vs 2.98, 16.90 vs 23.62, 0.84 vs 0.80, 16.88 vs 18.70, all P < 0.01 ). In the 4-variable model, random forest regression model showed an improvement in precision and RMSE compared with revised regression model. (20.82 vs 25.25, P < 0.01, 19.08 vs 20.60, P < 0.001). Bias and 30%accurancy were preferable, but the results were not statistically significant (0.34 vs 2.07, P = 0.10, 0.8 vs 0.78, P = 0.19, respectively). Conclusions The performances of random forest regression models are better than revised linear regression models when it comes to GFR estimation.

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Section: Discussionmentioning

confidence: 93%

Applying Random Forest Model Algorithm to GFR estimation

Yang

Chong

et al. 2020

Preprint

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“…LR is a type of supervised ML algorithm that is used to predict continuous outcomes using a constant slope [32]. Since there are 8 independent variables, the polynomial regression is not a suitable method.…”

Section: Lrmentioning

confidence: 99%

Prediction of Dimensional Changes of Low-cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

Zhang¹,

Femi-Oyetoro²,

Fidan³

et al. 2021

Preprint

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Additive manufacturing (AM) is an emerged layer-by-layer manufacturing process. However, its broad adoption is still hindered by limited material options, different fabrication defects, and inconsistent part quality. Material extrusion (ME) is one of the most widely used AM technologies, and, hence, is adopted in this research. Low-cost metal ME is a new and AM technology used to fabricate metal composite parts using sintered metal infused filament material. Since the involved materials and process are relatively new, there is a need to investigate the dimensional accuracy of ME fabricated metal parts for real-world applications. Each step of the manufacturing process, from the material extrusion to sintering, might significantly affect the dimensional accuracy. This research provides a comprehensive analysis of dimensional changes of metal samples fabricated by the ME and sintering process, using statistical and machine learning algorithms. Machine learning (ML) methods can be used to assist researchers in sophisticated pre-manufacturing planning and product quality assessment and control. This study compares linear regression to neural networks in assessing and predicting the dimensional changes of ME made components after 3D printing and sintering process. The prediction outcomes using a neural network performed the best with the highest accuracy as compared to regression. The findings of this study can help researchers and engineers to predict the dimensional variations and optimize the printing and sintering process parameters to obtain high quality metal parts fabricated by the low-cost ME process.

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“…The most commonly used kernel functions include: linear kernel, polynomial kernel, sigmoid kernel, radial basis function (RBF) kernel, etc. [50]. Using learning curve, RBF was selected as the kernel function in this research:…”

Section: Random Forestmentioning

confidence: 99%

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Huang¹,

Guo²,

Chen³

et al. 2020

Preprint

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Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R 2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

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A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

Cited by 250 publications

References 46 publications

Applying Random Forest Model Algorithm to GFR estimation

Applying Random Forest Model Algorithm to GFR estimation

Prediction of Dimensional Changes of Low-cost Metal Material Extrusion Fabricated Parts Using Machine Learning Techniques

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

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