Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence in coal mine areas. Landslide data collected by the Bureau of Land and Resources are represented by the X, Y coordinates of its central point; causative factors were calculated from topographic and geologic maps, as well as satellite imagery. The five-fold cross-validation method was adopted and the landslide/non-landslide datasets were randomly split into a ratio of 80:20. From this, five subsets for 20 times were acquired for training and validating models by GIS Geostatistical analysis methods, and all of the subsets were employed in a spatially balanced sample design. Three landslide models were built using support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models by selecting the median of the performance measures. Then, the three fitted models were compared using the area under the receiver operating characteristics (ROC) curves (AUC) and the performance measures. The results show that the prediction accuracies are between 73.43% and 87.45% in the training stage, and 67.16% to 73.13% in the validating stage for the three models. AUCs vary from 0.807 to 0.906 and 0.753 to 0.944 in the two stages, respectively. Additionally, three landslide susceptibility maps were obtained by classifying the range of landslide probabilities into four classes representing low (0–0.02), medium (0.02–0.1), high (0.1–0.85), and very high (0.85–1) probabilities of landslides. For the distributions of landslide and area percentages under different susceptibility standards, the SVM model has more relative balance in the four classes compared to the LR and the ANN models. The result reveals that the SVM model possesses better prediction efficiency than the other two models. Furthermore, the five factors, including lithology, distance from the road, slope angle, elevation, and land-use types, are the most suitable conditioning factors for landslide susceptibility mapping in the study area. The mining disturbance factor has little contribution to all models, because the mining method in this area is underground mining, so the mining depth is too deep to affect the stability of the slopes

Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area

Landslide susceptibility mapping is the first and most important step involved in landslide hazard assessment. The purpose of the present study is to compare three nonlinear approaches for landslide susceptibility mapping and test whether coal mining has a significant impact on landslide occurrence in coal mine areas. Landslide data collected by the Bureau of Land and Resources are represented by the X, Y coordinates of its central point; causative factors were calculated from topographic and geologic maps, as well as satellite imagery. The five-fold cross-validation method was adopted and the landslide/non-landslide datasets were randomly split into a ratio of 80:20. From this, five subsets for 20 times were acquired for training and validating models by GIS Geostatistical analysis methods, and all of the subsets were employed in a spatially balanced sample design. Three landslide models were built using support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models by selecting the median of the performance measures. Then, the three fitted models were compared using the area under the receiver operating characteristics (ROC) curves (AUC) and the performance measures. The results show that the prediction accuracies are between 73.43% and 87.45% in the training stage, and 67.16% to 73.13% in the validating stage for the three models. AUCs vary from 0.807 to 0.906 and 0.753 to 0.944 in the two stages, respectively. Additionally, three landslide susceptibility maps were obtained by classifying the range of landslide probabilities into four classes representing low (0–0.02), medium (0.02–0.1), high (0.1–0.85), and very high (0.85–1) probabilities of landslides. For the distributions of landslide and area percentages under different susceptibility standards, the SVM model has more relative balance in the four classes compared to the LR and the ANN models. The result reveals that the SVM model possesses better prediction efficiency than the other two models. Furthermore, the five factors, including lithology, distance from the road, slope angle, elevation, and land-use types, are the most suitable conditioning factors for landslide susceptibility mapping in the study area. The mining disturbance factor has little contribution to all models, because the mining method in this area is underground mining, so the mining depth is too deep to affect the stability of the slopes

Designing grazing systems that enhance the health of New Zealand high-country grasslands : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

Grazing management enabling pastoral livestock-production systems to deliver multiple ecosystem services is key to assure the long-term health and stability of grasslands. In the context of designing multi-functional grazing systems to enhance grassland health, systems thinking emerges as a useful tool to understand, modulate and enhance the resilience of those systems. The objective of the outlined research was to apply systems thinking and design theory to design alternative grazing systems that enhance grassland health using high-country stations in New Zealand as a model and Lincoln University Mount Grand Station (LUMGS) as the case study. This was conducted over five modelling exercises described as the design method to design scenarios representing distinct grazing management that enhance grassland health in different ways. The first design step applied spatial analysis to create a modern rich picture for grassland health diagnostic which determined that 97.7% of LUMGS grassland has a moderate health condition. Then, a geospatial modelling approach was used to assess the current capability of LUMGS in delivering ecosystem services. It was determined that LUMGS has a spatially variable potential for agriculture productivity, a high flood mitigation capacity, a high capacity of C sequestration, an extreme risk of erosion, a capacity to reduce sediment delivery to streams, and overall, a low to moderate nitrogen and phosphorus accumulation. Those ecosystem services were negatively affected by farming activities. Next, a geospatial modelling approach was used to understand the spatiotemporal impacts of different stock densities, grazing occupation periods, and stock types on soil susceptibility to erosion. Increases in the occupation period were more detrimental to soil loss than increases in stock density, and losses were greater for cattle than for sheep and deer. Those effects were spatially and temporally variable. In the following chapter, we applied a spatial-chemical analysis to grassland ecosystems for the illustration of chemoscapes and the creation of healthscapes. We created maps that show an extra perspective of plant nutritional value by illustrating their distribution over LUMGS according to their medicinal effects. Finally, by integrating all those design tools, three alternative grazing system scenarios were created and evaluated, from which a multi-criteria evaluation defined that the ‘best-compromise’ scenario to enhance grassland health is the scenario with lower soil erosion, the lower total emission of greenhouse gases, and greater profitability compared to the parsimonious approach of the ‘status quo’. The design methodology proposed in this thesis demonstrates that grasslands need to be managed as context-adjusted, adaptive, and complex systems to be multifunctional and continually deliver multiple ecosystem services to enhance grassland health