Calculation of mine reclamation bond based on the dominant factors affecting land destruction

Cheng Linlin, Jeffrey G. Skousen, Fu Yajie, Ma Lu, Dong Xuemei, Sun Haiyuan, Zhang Xiaoyu, He Zhilong and Teng Jiahua

In order to calculate mine reclamation bond scientifically and reasonably, this study put forward and verified the method and model based on the dominant factors affecting land destruction and calculated the total amount of mine reclamation bond of Dongtan coal mine, Yanzhou mining area, Shandong Province. First, the general factors affecting land destruction were determined through theoretical analysis. Then, the surface subsidence value was selected as a measure of the degree of land destruction in the study area. The dominant factors affecting land destruction in Yanzhou mining area were selected by analyzing grey correlation degree between the value of surface subsidence and the general factors and their action mechanisms were analyzed. The dominant factors were classified and evaluated and their weights were determined. The calculation units of Dongtan coal mine were assessed by the comprehensive condition index of the dominant factors affecting land destruction. Reclamation cases were selected to verify the calculation model and the reclamation cost corresponding to per unit of comprehensive condition index of the dominant factors affecting land destruction of different damage degrees in each reclamation direction was calculated. The total bond amount Dongtan coal mine should pay was then calculated by the calculation model. The result shows that the amount calculated by this method is much closer to the true reclamation cost, more objective, scientific and operable. Therefore, the calculation method based on the dominant factors affecting land destruction should be used to unify the different methods throughout the country.

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A Multi-Stage Stochastic Optimisation on Purchasing of Relief Items during Flood

Phuekfhon Arnon and Liu Nan

Purchasing of relief items is one of the critical elements in humanitarian logistics and supply chain when handling flood disaster relief, given the cost sensitivity and urgency of the need. There is little research, although uncertain, covering disaster relief problems and the incumbent purchasing decisions. However, decision making during disaster relief operations should not take place only pre-event or post-event, as just a two-stage stochastic programming. There are multiple stages in the decision-making process. This paper develops a multi-stage stochastic programme on purchasing of relief commodities during a disaster, using real data from flood scenarios in Thailand. A genetic algorithm has been developed to increase efficiency in stock management. The main problem is that there is both a surplus and a shortage of relief items during such situations. The computational results show the potential to rectify this imbalance and generate cost savings. This research aims to address both academia and policy makers result.

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Study of GA-BP Neural Network Method on predicting Regional Risk of Rock Burst

Jin Peijian, Sun Shimei and Huang Ning

GA-BP Neural Network Prediction Method for predicting the risk of rock burst is studied as a new method using genetic algorithm to optimize the BP neural network. A new model on evaluation of rock burst danger was built based on comprehensive considering of the influence factors of rock burst danger. Then, following the quantification processing, sample learning on the model was conducted according to coal mine field data and the model was verified by existing data. The results show that this method has accuracy evaluation results, high precision，fast operation and good practical value.

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