The stable isotope has been extensively applied as an effective tracer especially in precipitation. In glacierized area of arid northwest China, temperature is widely considered to be a major factor affecting isotopes in precipitation, while the influences of precipitation amount, relative humidity and other meteorological parameters are still not clear. Based on analyses on stable isotope values of water samples and NCEP/NCAR(National Centers of Environmental Prediction/National Center for Atmospheric Research, USA) re-analysis data, the moisture source and characteristics of isotopes in the precipitation, meltwater and river water isotopes at Urumqi Glacier No.1 of the upstream Urumqi River Basin, eastern Tianshan Mountains from spring to autumn during four years(from 2008 to 2011) was studied. Results indicated that meltwater are the main source of water for the upper Urumqi River. Seasonal variation of δ~(18) O in precipitation demonstrated that δ18 O was more enriched in summer and depleted in spring and autumn. Temperature was positively correlated with isotopes, while precipitation amount and relative humidity was negatively correlated with isotopes. The water vapor was affected by westerly air mass and regional water vapor cycle. Meanwhile, back trajectory clustering analyses showed that the moisture mainly from Europe and central Asia. The moisture was more likely to be locally sourced with the ratio was 46.8%～52.1%. 相似文献
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.
