A New Approach of Disaster Forecasting Based on Least Square Optimized Neural Network

Abstract

The evaluation of the risk is the prerequisite for the implementation of countermeasures in the prevention and control of rock burst, and the research on the fast forecast at scene of the rock burst is more important for the safety production of coal mine. Aiming at the problem that dynamic disasters caused by many factors and heterogeneity of coal and rock are difficult to predict in the process of coal mining, in this paper, the general law and the risk control factors of the rock burst are studied, a mathematical model based on the BP neural network was built according to the different actual mining conditions in the mining area, and the output layer has obtained the prediction result. Then, the results of the output samples after training were fitted by using SPSS software, and the fitting function was obtained by multiple least square fitting. Finally, the fitting results are checked by the data of actual coal mine dynamic disaster parameters. The prediction results show that the simulation results of BP neural network prediction model and the fitting function of the least square method can reduce the impact of subjective judgment on the prediction results, and the application of the fitting function can obtain the prediction results in the first time to ensure the construction safety. The method of on-site hazard assessment and inspection by using fitting function is simple and feasible and has high accuracy, which provides a new idea for the field prediction of rock burst.

Prediction of Grade Classification of Rock Burst Based on PCA-SSA-PNN Architecture

“…Considering the actual complexity, many scholars adopted the multi-index method to estimate the rock burst grade. For example, Zhou et al [20] considered that genetic algorithm and particle swarm optimization algorithm could speed up the parameter optimization search of support vector machine (SVM), and the proposed method of rock burst grade prediction has strong robustness; Dong et al [21] found that compared with SVM, the random forest algorithm had a lower misjudgment rate of rock burst grade; Wang et al [22] established a multi-index method for rock burst prediction based on the fuzzy matter-element theory, information entropy theory, and proximity rule and found that the established method is more reliable than the traditional method; Zhang et al [23] made a comprehensive prediction of rock burst based on the rock elastic energy index, rock strength, and principal stress, which could make up for the deficiency of single-index rock burst prediction method; Li et al [24] proposed a rock burst prediction network based on genetic algorithm and extreme learning machine, and the prediction results show that the maximum relative error of the proposed method is 4.71%; Xu et al [25,26] put forward a new rock burst grade evaluation using the ideal point theory, and the error rate is 5%, and the average crossover error rate is 13.33%; Liang et al [27] found that gradient-boosted decision tree algorithm could be applied to short-term rock burst prediction with an accuracy of more than 90%; Meng et al [28] believed that BP (back propagation) neural network prediction and least square method may reduce the influence of subjective judgment on the prediction results and could obtain the prediction results in the first time; Chen et al [29] utilized the Bayesian method to estimate the rock burst grade and found that Bayesian statistical learning model has robustness and generalization in rock burst risk assessment; Gao et al [30] held that the radial basis neural network optimized by hybrid particle swarm optimization algorithm may take into account individual optimization and global optimization and could predict the rock burst grade correctly and effectively; Gong et al [31] established a deep learning rock burst prediction model based on dropout and Adam algorithm, and the model avoids the problem of determining index weights and is completely data-driven; Liu et al [32] found that the rock burst prediction network based on histogram gradient-enhanced tree algorithm still has a high prediction ability for incomplete rock burst data, with an accuracy of nearly 80%. In these above researches, machine learning and deep learning methods were adopted to establish a multiindex rock burst grade prediction network, and the accuracy of the prediction results was significantly improved than that of the single-index rock burst prediction method.…”

Research and Application of Rock Burst Hazard Assessment of the Working Face Based on the CF-TOPSIS Method

“…Based on the cloud model and D-S theory, Chen [15] evaluated the hazard of rock burst, while Cai et al [16] further developed the spatiotemporal forecasting method for rock bursts using multidimensional microseismic information. To address the challenge of predicting dynamic disasters caused by various factors and the heterogeneity of coal and rock in mining operations, Meng et al [17] studied the general patterns and hazard control factors of rock bursts. Additionally, in regional prediction methods that involve "strong time and space" considerations, several techniques such as the generalized artifcial neural network [18], particle swarm optimization KNN, cloud model, and decision tree [19,20] have yielded promising results in rock burst prediction.…”

Assessment of Machine Learning Models for the Prediction of Rate-Dependent Compressive Strength of Rocks

“…In recent years, machine learning techniques such as artificial neural network (ANN), support vector machine (SVM), random forest (RF), extreme learning machine (ELM), and other models have been employed to predict concrete compressive strength and have achieved good prediction behavior [18][19][20][21][22][23][24][25]. Similarly, some intelligent models have been introduced to predict the mechanical properties and stability of rocks, with significant progress being made by numerous scholars [26][27][28][29][30][31][32][33][34]. For example, Ebrahim, et al [35] developed a model tree approach to predict the uniaxial compressive strength and elastic modulus of carbonate rocks, which provided better prediction results.…”