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.

In order to address the issue of high temperatures and thermal damages in deep mines, the factors causing downhole heat damage at high temperatures were analyzed, the mine ventilation system was optimized and rebuilt, and a cooling system was established. The proposed cooling system uses mine water as the cooling source, and its features are based on the analysis of traditional cooling systems. The current ventilation system in the 1118 m deep pit of the Jinqu Gold Mine was evaluated, and the ventilation network, ventilation equipment, and ventilation structures near the underground working face were optimized. The low-temperature mine water stored in the middle section of the mine at 640 m depth was used as the cooling source, and a cooling system was established near the 440 m deep middle return well to alleviate the high-temperature and high-humidity conditions of the 280 m deep middle-western area. The results show that the effective air volume in the west wing at 280 m was 3.0 m3/s, the operating ambient temperature was 27.6°C, the relative humidity was reduced to 76%, and the temperature was reduced by 5-6°C after the optimization of the system.

Resource-based cities are those where resource-based industries comprise a large proportion of all industries. Sustainable development implies that cities make full use of their own resources to support current development initiatives and take sustainability into account both during and after resource consumption. To promote investment in the sustainable development of resource-based cities and to a provide a decision system for these cities, this paper uses an ecological footprint model to evaluate and analyze the per capita ecological footprint, per capita ecological carrying capacity and per capita ecological deficit of a representative resource-based city, Yulin. The data are collected from 2001 to 2015. In addition, due to the complexity of the influencing factors for ecological carrying capacity and the variety of situations that are difficult to accurately predict, this paper proposes a new urban ecological carrying capacity prediction model, which consists of a radial basis function (RBF) neural network that is optimized by an improved artificial bee colony algorithm. The prediction results show that energy consumption is the major factor affecting the urban ecosystem; moreover, the model precision of the training results and the simulation accuracy of the test results achieved by the RBF neural network model are 97.91% and 94.16%, respectively, and in 2020, the per capita ecological footprint, biocapacity, and ecological deficit of Yulin are predicted to reach 4.892 hm 2 , 3.317 hm 2 , and 1.575 hm 2 , respectively. Accordingly, effective proactive measures should be taken in advance to maintain or reduce the ecological pressure on this resource-dependent city. This paper strives to provide a scientific basis for local government decision-making to realize the healthy, stable, and rapid sustainable development of resource-based cities. INDEX TERMS Resource-based city, ecological pressure prediction, RBF neural network, ABC algorithm, ecological footprint theory. I. INTRODUCTION As an integration of the topography, landform, soil, climate, hydrology, flora and fauna as well as the human activities The associate editor coordinating the review of this manuscript and approving it for publication was Yanzheng Zhu. in a certain region, the urban ecological environment is not only affected by human activities but also serves as the basis for human survival. Regarding resource-dependent cities, the ecological environment plays a vital role in such a special urban structure. A resource-based city is defined as a city in which the leading industries depend on the exploitation

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With the maturity of the Internet of Things, 5G communication, big data and artificial intelligence technologies, open-pit mine intelligent transportation systems based on unmanned vehicles has become a trend in smart mine construction. Traditional open-pit mine transportation systems rely on human power for command, which often causes vehicle delay and congestion. The operation of unmanned vehicles in an open pit mine relies on many sensors. Using big data from the sensors, we optimize vehicle paths and build an efficient intelligent transportation system. Based on large amounts of data, such as unmanned vehicle GPS data, vehicle equipment information, production plan data, etc., with the goal of reducing vehicle transportation costs, total unmanned vehicle delay time, and ore content fluctuation rate, a multiobjective intelligent scheduling model for open-pit mine unmanned vehicles was established, and it is aligned with actual open pit mine production. Next, we use artificial intelligence algorithms to solve the scheduling problem. To improve the convergence, distribution and diversity of the classical fast non-dominated sorting genetic algorithm (NSGA-II) to solve constrained high-dimensional multi-objective problems, we propose a decomposition-based constrained dominance principle genetic algorithm (DBCDP-NSGA-II), retaining feasible and non-feasible solutions in sparse areas, and compare it with four other commonly-used multiobjective optimization algorithms. Simulation analysis shows our algorithm provides the best overall performance results of the multi-objective models. Furthermore, we apply intelligent scheduling models and optimization algorithms to mining practice and obtain new truck operation routes and schedules, reducing truck operation costs by 18.2%, truck waiting time by 55.5%, and ore content fluctuation by 40.3%. For open-pit mine unmanned transportation, the approach provides a variety of optimized solutions for minimum transportation costs, minimum waiting time, minimum ore content fluctuation rate, and a balance of the three indicators. Through an artificial intelligence algorithm, this study realizes intelligent unmanned vehicle path planning and improves the operation efficiency of open-pit mine intelligent transportation systems. INDEX TERMS Intelligent transportation system, traffic big data, unmanned driving, intelligent scheduling, NSGA-II, open-pit mine.The associate editor coordinating the review of this manuscript and approving it for publication was J. D. Zhao .