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Founded on understanding of a slope’s likely failure mechanism, an early warning system for instability should alert users of accelerating slope deformation behaviour to enable safety-critical decisions to be made. Acoustic emission (AE) monitoring of active waveguides (i.e. a steel tube with granular internal/external backfill installed through a slope) is becoming an accepted monitoring technology for soil slope stability applications; however, challenges still exist to develop widely applicable AE interpretation strategies. The objective of this study was to develop and demonstrate the use of machine learning (ML) approaches to automatically classify landslide kinematics using AE measurements, based on the standard landslide velocity scale. Datasets from large-scale slope failure simulation experiments were used to train and test the ML models. In addition, an example field application using data from a reactivated landslide at Hollin Hill, North Yorkshire, UK, is presented. The results show that ML can automatically classify landslide kinematics using AE measurements with the accuracy of more than 90%. The combination of two AE features, AE rate and AE rate gradient, enable both velocity and acceleration classifications. A conceptual framework is presented for how this automatic approach would be used for landslide early warning in the field, with considerations given to potentially limited site-specific training data.

The resource security system is a complex system. Strategic mineral resource is of great importance to national security and economic development. Iron ore is one of the 24 strategic mineral resource determined by the Chinese government. Based on the broad connotation of resource security, we put forward the risk assessment model of resource security named 3E (Enduring, Economic, Environmental) model and establish a complete risk assessment index system for iron ore. In this paper, the entropy method is used to determine the weight of each index, we evaluate the comprehensive risk of iron ore from 2000 to 2016, and analyse the results of risk assessment in detail. The results show that the supply risk is on the rise, which has the greatest impact on the comprehensive risk. Moreover, the fluctuation of economic risk is more stable, and the environmental risk is decreasing. The comprehensive risk index of China’s iron ore increased firstly and then declined. From 2000 to 2007, it was a relatively low-risk period; from 2008 to 2010, the risk was increased; 2011-2013 was a high-risk period; and 2014 to 2016, there was a reduction in risk. In order to effectively control the risk of iron ore, this paper puts forward some suggestions from several aspects.

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