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The impact of the wetland ecosystem on arid and semi-arid areas is much higher than that in humid areas. It plays a more significant role in regulating climate, conserving water sources, purifying water bodies, and protecting biodiversity. The West Liao River Basin is located in a moderately temperate, semi-arid, and continental monsoon climatesensitive area, with a fragile ecological environment. Climate warming and drought have gradually caused dry-flow of some river sections in the basin, reduction in the water area, shrinking of wetlands, degradation of vegetation ecological function, and decline of biodiversity. Ultimately, the effect of ecological barriers is significantly weakened. The research of the temporal and spatial evolution of landscape patterns and their relationship with human activities in arid and semi-arid regions is of great significance for the protection and restoration of wetland resources. Based on Landsat remote sensing images in 1985 and 2015, the temporal and spatial evolution of landscape patterns in four key wetland areas in the West Liao River Basin was studied by 3S technology and landscape ecology indexes. Results show that during the 30a years, the wetland resources area in the West Liao River Basin is drastically reduced. Wetland resources of the four study areas, the Dalinor, the Saihanwula, the Hongshan Reservoir, and the West Liao River Estuary, were decreased by 13.80%, 31.06%, 61.10%, and 66.03%, respectively. The reduced wetland resources in the Dalinor and the Saihanwula were mainly converted into grassland, while those of the Hongshan Reservoir and the West Liao River Estuary were mostly converted into farmland. The diversity and evenness indexes of landscape in the Hongshan Reservoir and the West Liao River Estuary are gradually decreasing. The diversity and evenness indexes of the Dalinor and the Saihanwula are not significantly changed but are slightly disturbed by human activities. Changes in the landscape pattern index highlight the decrease in the water area, the increase in the area of dry and paddy fields, and the improvement of sandy land. All these further reflect that insufficient water resources supplementation and increasing human disturbance have a profound impact on the landscape pattern, which has also become the main driving force for the evolution of the landscape pattern in the West Liao River Basin.

The tool life prediction model of tunnel boring machine(TBM) can well reflect the influence of geological structure and machine operation on tool life. In this paper, basing on big data to modeling is the innovation rather than traditional model of analytical dynamics. The author firstly introduces the principle of support vector machine, then the author perfectly integrates both normal and non-normal tool damage, and make them combine with the dynamic factors influence tool life. From the data gaining from mining a city subway line 4 point of view, because the support vector machine has the advantages of small dimension, transduction reasoning and so on, the author chose this way. First of all, the author uses the gray correlation method to select driving factors to form information matrix which includes daily digging mileage, rock rank, number of tool damage, thrust of the cutter, cutter speed, speed of boring. The second the author use k-cross-check and grid search to find the optimal parameters and the author select the optimal kernel function is Gaussian kernel function. At last the author use structural model calculated to predict life of the tool from subway line 5, at the same time the author obtain the prediction accuracy is 94.52%. It is shown that the regression model based on SVM can predict the life of tool in the next working interval, which can provide guidance for scheduling tools and intelligent use of shield machine.