Dynamic prediction model of spontaneous combustion risk in goaf based on improved CRITIC-G2-TOPSIS method and its application

Abstract

Due to the problems related to the numerous factors affecting the spontaneous combustion of goaf coal, such as sudden, uncertain, and dynamic changes, and the fact that the weight of the indexes in the prediction model of the spontaneous combustion risk is difficult to determine, an improved Criteria Importance Through Inter-criteria Correlation (CRITIC) modified Technique for Order of Preference by Similarity to Ideal Solution G2-(TOPSIS) dynamic prediction model of goaf spontaneous combustion was developed. An optimal decision-making model was established by introducing the Euclidean distance function, and the function-driven type G2 weighting method was modified using the differential-driven type weighting method of the CRITIC. In addition, the comprehensive weights of each index were obtained. An update factor was introduced to obtain the dynamic weight, and the primary-secondary relationship of the risk factors affecting the spontaneous combustion of goaf was evaluated. Based on the G2 weighting method, which approximates the driving function principle of the ideal solution ranking method (TOPSIS), a G2-TOPSIS goaf spontaneous combustion risk assessment model was established. The degree of closeness was analyzed and the risk grade of the goaf spontaneous combustion was finally predicted. The sub-model was applied to the goaf of working face 1303 in the Jinniu Coal Mine. It was concluded that the air leakage duration was the dominant factor inducing the risk of the spontaneous combustion of the goaf. The risk grade of spontaneous combustion of the goaf is Class III, and the predicted results are consistent with the actual situation. The improved CRITIC-G2-TOPSIS dynamic model was demonstrated to be scientific in predicting the goaf spontaneous combustion risk, and these research results have important popularization and application value.

Prediction Model of Borehole Spontaneous Combustion Based on Machine Learning and Its Application

“…To verify the accuracy of the predictions of the HGS-RF model, the RF, SSA-RF, PSO-RF and QPSO-RF models were introduced to predict the degree of spontaneous combustion in boreholes, and the predictions were compared and analyzed. The dimension of the parameter space D in HGS was 2, the population was 30, the maximum number of iterations was 120, L = 0.03, the lower bound of H was 100 and the range of values for the upper limit B U and lower limit B L of parameter space D was [10,1] and [200,50], respectively. The leaf parameters n in RF_ estimator and min_ samples_ were optimized by HGS with values of 100 and 2, respectively.…”

“…Qi Yun et al [9] used the ensemble evaluation method of the set-valued statistics Entropy to model the human decision-making process and treated multidimensional data resulting from spontaneous combustion of coal to avoid bias in quantitative evaluation. Wang Wei et al [10] modified the G2 weighting method by improving the CRITIC method and combined it with the TOPSIS method to construct a risk assessment model for spontaneous combustion in drilling, effectively evaluating the natural hazards of drilling. Zhou Xu et al [11] constructed a predictive model for the coal ignition temperature based on the PSO-XGBost algorithm and compared it with the RF model and the GBRT model, noting that the constructed model is better in accuracy and robustness.…”

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

“…Traditional CRITIC uses standard deviation as its conflict criterion, but each index used in this paper has great difference and negative correlation, so single standard deviation can not reflect the difference [53]. Therefore, based on the basic principle of CRITIC, this paper uses the coefficient of variation to replace the standard deviation to express the index contrast strength [54,55]. Through the above Equation, we can get the correlations of Unified scheduling load, Inter-provincial demand load, New energy output and Thermal power output: 0.8214, 0.5790, −0.7954, and 0.9655, respectively.…”

“…Traditional CRITIC uses standard deviation as its conflict criterion, but each index used in this paper has great difference and negative correlation, so single standard deviation can not reflect the difference [53]. Therefore, based on the basic principle of CRITIC, this paper uses the coefficient of variation to replace the standard deviation to express the index contrast strength [54,55].…”

Prediction model of spontaneous combustion risk of extraction borehole based on PSO-BPNN and its application

“…XING Yuanyuan et al 15 used the inverse entropy weighting method to determine the weights of evaluation indexes based on the principle of minimum information identification, and then constructed a coal spontaneous combustion risk evaluation model based on the TOPSIS method. Wang Wei et al 16 proposed a dynamic weighting method, and then established a dynamic prediction model for the risk of coal spontaneous combustion according to the characteristics of dynamic changes in the goaf. Shuang et al 17 proposed an improved grey wolf optimized support vector regression coal spontaneous combustion temperature prediction model based on nonlinear parameter control, dynamic inertia weights and grey wolf social hierarchy.…”