Predicting the deformation and evolution tendency of landslides is essential to landslide disaster prevention and mitigation. At present, most of the proposed models for landslide displacement prediction belong to single models. It is difficult to accurately describe the deformation and evolution law only by a single model for the complexity of landslides and limitation of the models. In this paper, we presented an application of linear combination model with optimal weight in landslide displacement prediction. We took Huanlongxicun and Saleshan landslides in Gansu province of China as examples, firstly to build GM(1,1) and Verhulst models for displacement prediction of the two landslides; then build two linear combination models of the two landslides, on the basis of the combining theory with optimal weight and the prediction results of the GM(1,1) and Verhulst models. The results show that the prediction accuracies of the combining models are much higher than those of the single models for both Huanglongxicun landslide and Saleshan landslide. Therefore, the combining model with optimal weight is an effective and feasible method to further improve accuracy for landslide displacement prediction.

Land degradation is a major threat to the sustainability of human habitation, and it is essential to assess it quantitatively. Assessment of the human-induced aspect is especially important for planning appropriate prevention measures. This paper used the Three-North Shelter Forest Program region as the study area, and assessed the land degradation dynamic using a time series of summed normalized difference vegetation index (NDVI) based on a trend analysis of the Theil-Sen slope and Mann-Kendall test. The human-induced land degradation was separated from degradation driven by climate using the meteorological dataset through the residual trend (RESTREND) method for the period 1982-2006. The results showed that (1) the NDVI in the study area mainly exhibited an increasing trend, approximately 13.00% of the study area experienced significantly positive NDVI trends and 6.20% showed decline. Furthermore, (2) the correlation between the summed NDVI and precipitation was higher than the correlation between NDVI and temperature, suggesting that precipitation was the most essential factor that impacted NDVI dynamic in the study area; (3) The significant trends of vegetation by anthropogenic disturbances were detected, which were significant positive and negative trends of 11.93% and 6.19%, respectively. All of these findings enrich our knowledge of human activities that impact land degradation in arid or semi-arid regions and provide a scientific basis for the management of ecological restoration programs.

Abstract. Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multifactor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multifactor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.

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For two unequal collinear cracks under compression, which crack would propagate first, the longer one or the shorter one, and how they affected each other was studied. By using complex stress function theory and considering crack surface friction, the analytical formula of stress intensity factors (SIFs) for an infinite plane containing two unequal collinear cracks was obtained and the analytical results have been validated through numerical simulation by employing ABAQUS code, photoelastic experiments, and compressive tests. The results show that the numerical, photoelastic, and compressive test results agree well with the analytical result. Finally, the effects of crack length, crack surface friction, and crack interval distance between two crack tips on SIFs were analyzed, and the results show that the SIF values at the longer crack tips are always higher than those at the shorter crack tips; for each crack, the SIF value at the internal tip is always larger than that at the external tip.

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