Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope

Complexity, Aug 2018

With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform.

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Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope

Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope Song Jiang,1 Minjie Lian,1,2 Caiwu Lu,1 Qinghua Gu,1 Shunling Ruan,1 and Xuecai Xie3 1School of Management, Xi’an University of Architecture and Technology, Shaanxi 710055, China 2Sinosteel Mining Co. Ltd., Beijing 100080, China 3College of Resource and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China Correspondence should be addressed to Qinghua Gu; moc.621@ugauhgniq Received 7 April 2018; Revised 19 May 2018; Accepted 30 May 2018; Published 1 August 2018 Academic Editor: Zhihan Lv Copyright © 2018 Song Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform. 1. Introduction Landslide is one of the most worldwide and dangerous natural disasters, which is a serious life threat for people working at open-pit mine slopes. Because of the complexity and uncertainties of the landslide formation and causing factors, landslide surveillances and predictions are always highly paid attention to by international researchers. With the wild application of systematic science and nonlinear science, researchers realized that landslide body is an open and complex system. By using sensor anomaly monitoring technology, massive amount of alert logs will be generated in the process of microseismic monitoring. These logs contain the time, place, alert level, and other relevant data of anomalous incidents. There are many researchers which have investigated the microseismic monitoring models in recent years [1–3]. Anomaly monitoring techniques are varied based on different types of analysis subjects. Among those, monitoring methods based on anomaly history and system status are most generally applied. Previously, researchers have focused on factors such as intrusion detection system [4, 5], prediction model [6, 7], and system call [8, 9]. Nohra et al. [10] suggest that near continuous monitoring via infrared spectroscopy is safe and accurate for use in critically ill surgical and trauma patients. Vinoth et al. [11] propose that PC-based microseismic network consisting of geophones, data loggers, GPS synchronization, and Ethernet antennas for wireless communication is employed to study the impact of induced seismicity on the slope failures in real time. Monitoring method based on anomaly history majorly collects the anomalous data of complex systems (e.g., slope microseismic monitoring alert logs). Machine learning and data mining are used in anomaly analysis in order to discover the potential correlations between history anomaly and future anomaly, which helps the prediction of future landslides. Many methods have been proposed in the literature such as neural network [12, 13]. Based on modified genetic algorithm, back propagation neural network classification algorithm, and the landslide disaster prediction theory, taking into account the rainfall and other uncertainties in landslide, this paper proposes the concept of separation of uncertain data/elaborates the processing methods of uncertain property data and builds uncertain genetic neural network and the landslide hazard prediction model. However, the method often results in such problems as local minimization and slow convergence rate. Most of previous researches only employ dichotomy algorithm of SVM [14, 15], which is far from enough for anomaly monitoring system in open-pit mine. There are also methods used in this area, such as grey prediction model [16, 17], fuzzy clustering [18, 19], and numerical simulation [20–22], which also have its respective disadvantages that will not be listed one by one. With the coming of big data age, data collected is growing in an exponential rate. In the areas of (...truncated)


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Song Jiang, Minjie Lian, Caiwu Lu, Qinghua Gu, Shunling Ruan, Xuecai Xie. Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope, Complexity, 2018, 2018, DOI: 10.1155/2018/1048756