The Impact of Landscape Characteristics on Groundwater Dissolved Organic Nitrogen: Insights From Machine Learning Methods and Sensitivity Analysis

2018 ◽  
Vol 54 (7) ◽  
pp. 4785-4804 ◽  
Author(s):  
B. Wang ◽  
M. R. Hipsey ◽  
S. Ahmed ◽  
C. Oldham
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Dissolved Organic Nitrogen ◽  
Organic Nitrogen ◽  
Learning Methods ◽  
Landscape Characteristics ◽  
Machine Learning Methods ◽  
The Impact

scholarly journals Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7232
Author(s):  
Costel Anton ◽  
Silvia Curteanu ◽  
Cătălin Lisa ◽  
Florin Leon
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Raw Materials ◽  
Optimization Procedure ◽  
Exhaust Emission ◽  
Energy Potential ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Emission Changes ◽  
The Impact

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.

scholarly journals Quantified uncertainties in fission yields from machine learning

2020 ◽  
Vol 242 ◽  
pp. 05003
Author(s):  
A.E. Lovell ◽  
A.T. Mohan ◽  
P. Talou ◽  
M. Chertkov
Keyword(s):  
Machine Learning ◽  
Nuclear Physics ◽  
Experimental Error ◽  
Training Data ◽  
Learning Methods ◽  
Mixture Density ◽  
Machine Learning Methods ◽  
Predicted Values ◽  
The Impact ◽  
Fission Yields

As machine learning methods gain traction in the nuclear physics community, especially those methods that aim to propagate uncertainties to unmeasured quantities, it is important to understand how the uncertainty in the training data coming either from theory or experiment propagates to the uncertainty in the predicted values. Gaussian Processes and Bayesian Neural Networks are being more and more widely used, in particular to extrapolate beyond measured data. However, studies are typically not performed on the impact of the experimental errors on these extrapolated values. In this work, we focus on understanding how uncertainties propagate from input to prediction when using machine learning methods. We use a Mixture Density Network (MDN) to incorporate experimental error into the training of the network and construct uncertainties for the associated predicted quantities. Systematically, we study the effect of the size of the experimental error, both on the reproduced training data and extrapolated predictions for fission yields of actinides.

scholarly journals Daily streamflow forecasting for Paraíba do Sul river using machine learning methods with hydrologic inputs

2018 ◽  
Author(s):  
Yulia Gorodetskaya ◽  
Leonardo Goliatt Da Fonseca ◽  
Gisele Goulart Tavares ◽  
Celso Bandeira de Melo Ribeiro
Keyword(s):  
Machine Learning ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Learning Methods ◽  
Daily Streamflow ◽  
Machine Learning Methods ◽  
Strategic Value ◽  
The Impact ◽  
Hydrologic Inputs ◽  
Paraíba Do Sul River

The Paraíba do Sul river flows through the most important industrial region of Brazil and its basin is characterized by conflicts of multiple uses of its water resources. The prediction of its natural flow has strategic value for water management in this basin. This research investigates the applicability of the two machine learning methods (Random Forest and Artificial Neural Networks) for daily streamflow forecasting of the Paraíba do Sul River at lead times of 1-7 days. The impact of fluviometric and pluviometric data from other basin sites on the quality of the forecast is also evaluated.

scholarly journals Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yue You ◽  
Svetlana V. Doubova ◽  
Diana Pinto-Masis ◽  
Ricardo Pérez-Cuevas ◽  
Víctor Hugo Borja-Aburto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Sensitivity Analysis ◽  
Glycemic Control ◽  
Family Medicine ◽  
Patient Data ◽  
Learning Approaches ◽  
Learning Methods ◽  
The Impact

Abstract Background The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. Methods We analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders. Results The study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMC’s delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics. Conclusions DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers.

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