A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler

Lv, You; Liu, Jizhen; Yang, Tingting; Zeng, Deliang

doi:10.1016/j.energy.2013.02.062

Cited by 173 publications

(67 citation statements)

References 35 publications

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“…To overcome these inadequacies, we use the least-squares support vector machine (LSSVM) proposed by Suykens and Vandewalle (1999), which is a supervised learning model that has been widely applied in other machine learning problems, such as function fitting. The LSSVM model uses the square sum of the least-squares linear system error as the loss function and solves the problem by transforming it into a set of equations, which increases the solution speed and reduces the required calculation resources (Suykens et al, 2002;Lv et al, 2013;Xu and Chen, 2013;Zhang et al, 2013). Additionally, this method yields good performance in pattern recognition and nonlinear function fitting.…”

Section: Introductionmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

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Section: Introductionmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…A study [20] used artificial neural networks to build a prediction model for CO 2 , soot, and NO x . In references [19,21], an adaptive least squares support vector machine model was built for the prediction of NO x emissions with a novel update to tackle process variations. The support vector machine (SVM), which is established based on the structural risk minimization principle, has proven to exhibit better generalization performance than neural networks and other methods [22].…”

Section: Experimental Setup and Methodsmentioning

confidence: 99%

Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

Liu

Yan

et al. 2016

Sustainability

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Abstract:The objective of the study is to present the modeling and multi-objective optimization of NO x conversion efficiency and NH 3 slip in the Selective Catalytic Reduction (SCR) catalytic converter for a diesel engine. A novel ensemble method based on a support vector machine (SVM) and genetic algorithm (GA) is proposed to establish the models for the prediction of upstream and downstream NO x emissions and NH 3 slip. The data for modeling were collected from a steady-state diesel engine bench calibration test. After obtaining the two conflicting objective functions concerned in this study, the non-dominated sorting genetic algorithm (NSGA-II) was implemented to solve the multi-objective optimization problem of maximizing NO x conversion efficiency while minimizing NH 3 slip under certain operating points. The optimized SVM models showed great accuracy for the estimation of actual outputs with the Root Mean Squared Error (RMSE) of upstream and downstream NO x emissions and NH 3 slip being 44.01ˆ10´6, 21.87ˆ10´6 and 2.22ˆ10´6, respectively. The multi-objective optimization and subsequent decisions for optimal performance have also been presented.

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“…where W represents the weight vectors of factors, F represents the fuzzy membership matrix, m is the factor number, n is the class number, and f mn is the fuzzy membership for factor m of class n. The final evaluation is determined by the maximum membership principle (Liang et al 2003;Lv et al 2013). Fuzzy membership r A (x) is used to characterize the extent of element x belonging to fuzzy set A, whose values lie between 0 and 1.…”

Section: Fuzzy Comprehensive Evaluationmentioning

confidence: 99%

A data-driven approach for landslide susceptibility mapping: a case study of Shennongjia Forestry District, China

Chen

Han

Huang

et al. 2018

Geomatics, Natural Hazards and Risk

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The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a datadriven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions.

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A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler

Cited by 173 publications

References 35 publications

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Modeling and Multi-Objective Optimization of NOx Conversion Efficiency and NH3 Slip for a Diesel Engine

A data-driven approach for landslide susceptibility mapping: a case study of Shennongjia Forestry District, China

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