Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity

Pourbasheer, Eslam; Riahi, Siavash; Ganjali, Mohammad Reza; Norouzi, Parviz

doi:10.1016/j.ejmech.2009.09.006

Cited by 99 publications

(53 citation statements)

References 31 publications

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“…According to some research results (Lessmann et al, 2005;Pourbasheer et al, 2009), the GA has the advantages of reducing the blindness of artificial selection and enhancing the discrimination ability of the LSSVM model. Modeling with this method can achieve high precision if the training samples are reliable.…”

Section: Ga-lssvm Modelmentioning

confidence: 99%

“…In recent years, grey system models, time series models, neural network models, extreme learning machines, sup2182 T. Wen et al: Landslide displacement prediction using the GA-LSSVM model for landslide displacement prediction (Wang, 2003;Pradhan et al, 2014;Gelisli et al, 2015;Goetz et al, 2015;Kavzoglu et al, 2015). Previously, landslide susceptibility maps were assessed using a back propagation (BP) artificial neural network and logistic regression analysis (Nefeslioglu et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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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: Ga-lssvm Modelmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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“…It has the advantages of global optimality, implicit parallelism, high stability and wide usability. The method has been widely used in computer science, engineering management and social science (Lessmann et al, 2005;Pourbasheer et al, 2009;Choudhry and Garg, 2009). In this study, we mainly use GA to search for the parameters (C and γ ) of the SVM model for landslide development prediction.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Genetic algorithm (GA) is a global optimization algorithm with good robustness, which was first suggested by John Holland in 1975 (Goldberg andHolland, 1988). GA can be used to automatically recognize some parameters of SVMs (Lessmann et al, 2005;Pourbasheer et al, 2009). Hence, we present an application of GA-SVM in landslide development prediction.…”

Section: Introductionmentioning

confidence: 99%

Application of GA–SVM method with parameter optimization for landslide development prediction

Jiming

2014

Nat. Hazards Earth Syst. Sci.

109

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Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multifactor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multifactor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.

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“…The population size was varied between 50 and 300 for different GA runs. For a typical run, the evolution of the generation was stopped, when 90% of the generations had taken the same fitness [27,28]. In this paper, size of the population is 30 chromosomes, the probability of initial variable selection is 5:V (V is the number of independent variables), crossover is multi Point, the probability of crossover is 0.5, mutation is multi Point, the probability of mutation is 0.01 and the number of evolution generations is 1000.…”

Section: Genetic Algorithm For Descriptor Selectionmentioning

confidence: 99%

Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent

Shahpar¹,

Esmaeilpoor

2017

Chem. Method.

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A quantitative structure-retention relationship (QSRR), was developed by using the genetic algorithm-partial least square (GA-PLS), Kernel partial least square (GA-KPLS) and LevenbergMarquardt artificial neural network (L-M ANN) approach for the prediction of the retention time (RT) of the doping agents in urine. The values of the retention time were obtained by using ultra-high-pressure liquid chromatography-quadrupole time-offlight mass spectrometry (UHPLC-QTOF-MS). A suitable set of the molecular descriptors was calculated and the important descriptors were selected by the aid of the GA-PLS and GA-KPLS. By comparing the results, GA-KPLS descriptors are selected for L-M ANN. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. This model was used to predict the RT values of some of doping agents which were not used in the modeling procedure. This is the first research on the QSRR of doping agents against the RT using the GA-PLS, GA-KPLS and L-M ANN model. KEYWORDS Graphical AbstractApproach to Chemometrics Models … P a g e | 106

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Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity

Cited by 99 publications

References 31 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

Application of GA–SVM method with parameter optimization for landslide development prediction

Approach to chemometrics models by artificial neural network for structure: first applications for estimation retention time of doping agent

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