Study on risk control of water inrush in tunnel construction period considering uncertainty

Water inrush risk is a bottleneck problem affecting the safety and smooth construction of tunnel engineering works, so the risk control of water inrush is important, however, geological uncertainty and artificial uncertainty always accompany tunnel construction. Uncertainty will not only affect the accuracy of water inrush risk assessment results, but also affect the reliability of water inrush risk decision-making results. How to control the influence of uncertainty on water inrush risk is key to solving the problem of water inrush risk control. Based on the definition of improved risk, a risk analysis model of water inrush based on a fuzzy Bayesian network is constructed. The main factors affecting the risk of water inrush are determined by sensitivity analysis, and possible schemes in risk control of water inrush are proposed. Based on the characteristics of risk control of water inrush in a tunnel, a multi-attribute group decision-making model is constructed to determine the optimal water inrush risk control scheme, so that the optimal scheme for reducing uncertainty in risk control of water inrush is determined. Finally, this system is applied to Shiziyuan Tunnel. The results show that the proposed risk control system for reducing uncertainty of water inrush is efficacious. First published online 21 August 201

First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China

An accurate spatial distribution map of the urban dominant tree species is crucial for evaluating the ecosystem service value of urban forests and formulating urban sustainable development strategies. Spaceborne hyperspectral remote sensing has been utilized to distinguish tree species, but these hyperspectral data have a low spatial resolution (pixel size ≥ 30 m), which limits their ability to differentiate tree species in urban areas characterized by fragmented patches and robust spatial heterogeneity. Zhuhai-1 is a new hyperspectral satellite sensor with a higher spatial resolution of 10 m. This study aimed to evaluate the potential of Zhuhai-1 hyperspectral imagery for classifying the urban dominant tree species. We first extracted 32 reflectance bands and 18 vegetation indices from Zhuhai-1 hyperspectral data. We then used the random forest classifier to differentiate 28 dominant tree species in Shenzhen based on these hyperspectral features. Finally, we analyzed the effects of the classification paradigm, classifier, and species number on the classification accuracy. We found that combining the hyperspectral reflectance bands and vegetation indices could effectively distinguish the 28 dominant tree species in Shenzhen, obtaining an overall accuracy of 76.8%. Sensitivity analysis results indicated that the pixel-based classification paradigm was slightly superior to the object-based paradigm. The random forest classifier proved to be the optimal classifier for distinguishing tree species using Zhuhai-1 hyperspectral imagery. Moreover, reducing the species number could slowly improve the classification accuracy. These findings suggest that Zhuhai-1 hyperspectral data can identify the urban dominant tree species with accuracy and holds potential for application in other cities