Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
The Liaodong Shoal is a group of linear sand ridges located in the east Bohai Sea of China.In this study,54 surface sediment samples have been collected,current measurements at 4 stations have been carried out and bathymetric data were obtained.The current directions are rightward deflected relative to the strikes of the sand ridges.Affected by the narrowing effect of the ridge,the current velocities exhibited an anti-‘C’type vertical profile.The velocities of the lower currents linearly correlate with the water depths.The near-bed current velocities over the troughs are estimated to be higher than those over the ridges,and this feature could be explained by the loss of kinetic energy together with the conversion between kinetic energy and gravitational potential energy.The sedimentary characteristics that are compatible with the tidal dynamics are developed across the ridges and troughs,including grain size compositions,grain size parameters,mineral compositions and Dhm indexes.The existence of the angles between the current directions and the strikes of the sand ridges is the key factor for the growth of the sand ridges.The asymmetric hydrodynamic features between the flood and ebb currents lead to the differences in the topographical and sedimentary characteristics on both sides of a sand ridge.Insufficient material supply led to the degradation of the sand ridges,and the reduction of the tidal current intensity has led to the development of the subordinate sand ridges in the troughs.Sand ridges are migrating. 相似文献
This paper focuses on the contribution of the global positioning system (GPS) and BeiDou navigation satellite system (BDS) observations to precise point positioning (PPP) ambiguity resolution (AR). A GPS + BDS fractional cycle bias (FCB) estimation method and a PPP AR model were developed using integrated GPS and BDS observations. For FCB estimation, the GPS + BDS combined PPP float solutions of the globally distributed IGS MGEX were first performed. When integrating GPS observations, the BDS ambiguities can be precisely estimated with less than four tracked BDS satellites. The FCBs of both GPS and BDS satellites can then be estimated from these precise ambiguities. For the GPS + BDS combined AR, one GPS and one BDS IGSO or MEO satellite were first chosen as the reference satellite for GPS and BDS, respectively, to form inner-system single-differenced ambiguities. The single-differenced GPS and BDS ambiguities were then fused by partial ambiguity resolution to increase the possibility of fixing a subset of decorrelated ambiguities with high confidence. To verify the correctness of the FCB estimation and the effectiveness of the GPS + BDS PPP AR, data recorded from about 75 IGS MGEX stations during the period of DOY 123-151 (May 3 to May 31) in 2015 were used for validation. Data were processed with three strategies: BDS-only AR, GPS-only AR and GPS + BDS AR. Numerous experimental results show that the time to first fix (TTFF) is longer than 6 h for the BDS AR in general and that the fixing rate is usually less than 35 % for both static and kinematic PPP. An average TTFF of 21.7 min and 33.6 min together with a fixing rate of 98.6 and 97.0 % in static and kinematic PPP, respectively, can be achieved for GPS-only ambiguity fixing. For the combined GPS + BDS AR, the average TTFF can be shortened to 16.9 min and 24.6 min and the fixing rate can be increased to 99.5 and 99.0 % in static and kinematic PPP, respectively. Results also show that GPS + BDS PPP AR outperforms single-system PPP AR in terms of convergence time and position accuracy. 相似文献
大气细颗粒物PM2.5污染引起的雾霾天气既与本地污染物排放密切有关,也受局地特殊的风场影响.本文以武汉城市区域为研究对象,分别研究了长江沿岸的江陆风环流、东湖沿岸湖陆风环流的形成与转化特征,发现江风、湖风开始时间均为07:00-08:00,一年中最大风速均能达到2 m/s左右,而春夏季江风的持续时间高于秋冬季,夏季湖风的持续时间高于春季.同时发现区域附近温度和相对湿度之间有明显的相关性,湿度变化趋势大体上跟温度变化趋势相反,且温度对相对湿度的影响存在一定的滞后性,延迟时间大约为1 h. 相似文献