Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

He, Hujun; An, Le; Liu, Wei; Zhang, Jian

doi:10.1155/2017/8793632

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…Therefore, the maximum measure identification criterion is not suitable, and the confidence recognition criterion is used. is set at a credible degree ( ≥ 0.5, usually a value of 0.6 or 0.7 is taken), 0 = min( : ∑ =1 ≥ , 1 ≤ ≤ ), and 0 is considered for the 0 evaluation level of [14][15][16][17][18][19][20].…”

Section: Evaluation Criterion the Classification Of The Evaluation Lmentioning

confidence: 99%

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An Improved Uncertainty Measure Theory Based on Game Theory Weighting

Tian

Jin

et al. 2019

Mathematical Problems in Engineering

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In the application of uncertainty measure theory, the determination method of index weight mainly includes the subjective weight determination method and the objective weight determination method. The subjective weight determination method has the disadvantages affected by the subjective preference of the decision-maker. The objective weight determination method often ignores the participation degree of the decision-maker, and when using the uncertainty measure evaluation model to perform multi-index classification evaluation, the credible degree recognition criterion is often used as the attribute recognition of the object to be measured, because the credible degree is taken by the subjective people, and the different values of different people have a great influence on the evaluation results. In order to solve the above problems in the uncertainty measure theory, this paper used the combination weighting of game theory to determine the optimal weight. At the same time, the credible degree recognition criterion was improved on the basis of the concept of minimum uncertainty measure distance, and a game theory-improved uncertainty measure optimization model was proposed. Finally, the validity of the model was proven by a case.

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Section: Evaluation Criterion the Classification Of The Evaluation Lmentioning

confidence: 99%

“…. , ), the objects to be evaluated are considered to be the closest to the classification pattern system, so the classification level of the objects to be evaluated belongs to 0 [20][21][22][23].…”

Section: Evaluation Criterion the Classification Of The Evaluation Lmentioning

confidence: 99%

An Improved Uncertainty Measure Theory Based on Game Theory Weighting

Tian

Jin

et al. 2019

Mathematical Problems in Engineering

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“…The classification level of the objects x i to be evaluated is K ¼ mind i 1 i k; k ¼ 1; 2; Á Á Á ; p ð Þ (He et al, 2017;Jin et al, 2017;Wan, 2004).…”

Section: Uncertainty Measure Theory and Its Optimizationmentioning

confidence: 99%

The evaluation of potential for the exploration and development of coalbed methane resources based on an improved uncertainty measure optimization model

Fang

et al. 2018

Energy Exploration & Exploitation

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The recognition criteria of credible degree in uncertainty measure theory are improved based on the concept of distance discriminant and used to evaluate the potential for exploration and development of coalbed methane. This paper uses a systematic approach to establish a mathematical model based on entropy weight and improved uncertainty measure theory. In this model, uncertainty influence factors were analyzed qualitatively and quantitatively using data for the Muli coalfield of Qinghai province, China. The uncertainty measure function is based on experimental data, and the weight of index is determined by entropy weight theory. Optimization credible degree recognition criteria were used to evaluate the potential for coalbed methane exploration and development of six mines in the Muli coalfield. These data indicate that evaluation results obtained using entropy weight and improved uncertainty measure theory have practical application and can effectively assess the exploration and development potential of coalbed methane in a coal basin.

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“…Because of the numerous and uncertainty factors that affect the collapse activity, many methods for predicting and evaluating the collapse geological hazards have emerged. For example, applied the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method to evaluate the risk of collapse disaster, Zhang et al (2009) used the factor weighted summation model of the improved analytic hierarchy process to evaluate the sensitivity of landslides induced by the earthquake of Beichuan County, Xue et al (2011) proposed the risk evaluation model of collapse disaster based on extension theory and fuzzy theory, Gao et al (2006) constructed a landslide collapse risk assessment model based on GIS and information quantity model, He et al, (2013He et al, ( , 2017 established a comprehensive evaluation model of collapse hazard based on uncertainty measure and an information entropy and distance discriminant analysis model for predicting the grade of collapse hazard, Liu (2016) used Newmark displacement calculation model and probability method etc. to evaluated the risk of landslide induced by volcanic eruption of Changbai mountain pool in the sky.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Highway Collapse Hazard Based on Rough Set and Support Vector Machine

Quan

Zhu

et al. 2022

Preprint

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The prediction of possibility and risk classification of collapse is an important issue in the process of highway construction in mountain area. Based on the principle of rough set and support vector machine, a landslide hazard prediction model was established. First of all, according to field investigation, an evaluation index system and a sample set of evaluation index data were established, the rough set decision table was constructed by preprocessing the original data based on the function classification of standard evaluation index, and then, the influence indexes of the collapse activity were reduced by rough set theory, and the main 9 indexes affecting the collapse activity as the key discriminant factors of support vector machine model, namely slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock were extracted. Then, taking the data of 13 post earthquake collapses provided by the author etc in Yingxiu-Wolong highway of Hanchuan County as training samples, the optimal model parameters were analyzed and calculated. When the penalty parameter C is 8 and the kernel parameter g is 0.5, the correct rate of cross-validation is 100%, and the model is optimal. At last, 4 other landslide data were tested, the discriminant results of the test sample data were compared with the results obtained by uncertainty measure and distance discriminant analysis. The results show that the discriminant results of the test sample data by RS-SVM were consistent with the results obtained by uncertainty measure and distance discriminant analysis, the accurate rate is 100%. The collapse hazard analysis model based on rough set and support vector machine can reduce the computation while ensuring the accuracy of evaluation, and better solve the small sample and nonlinear problems, can provide certain a good idea for collapse hazard evaluation in the future.

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Prediction Model of Collapse Risk Based on Information Entropy and Distance Discriminant Analysis Method

Cited by 7 publications

References 8 publications

An Improved Uncertainty Measure Theory Based on Game Theory Weighting

An Improved Uncertainty Measure Theory Based on Game Theory Weighting

The evaluation of potential for the exploration and development of coalbed methane resources based on an improved uncertainty measure optimization model

Evaluation of Highway Collapse Hazard Based on Rough Set and Support Vector Machine

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