Relationship between Winter Snow Cover Dynamics, Climate and Spring Grassland Vegetation Phenology in Inner Mongolia, China

The onset date of spring phenology (SOS) is regarded as a key parameter for understanding and modeling vegetation–climate interactions. Inner Mongolia has a typical temperate grassland vegetation ecosystem, and has a rich snow cover during winter. Due to climate change, the winter snow cover has undergone significant changes that will inevitably affect the vegetation growth. Therefore, improving our ability to accurately describe the responses of spring grassland vegetation phenology to winter snow cover dynamics would enhance our understanding of changes in terrestrial ecosystems due to their responses to climate changes. In this study, we quantified the spatial-temporal change of SOS by using the Advanced Very High Resolution Radiometer (AVHRR) derived Normalized Difference Vegetation Index (NDVI) from 1982 to 2015, and explored the relationships between winter snow cover, climate, and SOS across different grassland vegetation types. The results showed that the SOS advanced significantly at a rate of 0.3 days/year. Winter snow cover dynamics presented a significant positive correlation with the SOS, except for the start date of snow cover. Moreover, the relationship with the increasing temperature and precipitation showed a significant negative correlation, except that increasing Tmax (maximum air temperature) and Tavg (average air temperature) would lead a delay in SOS for desert steppe ecosystems. Sunshine hours and relative humidity showed a weaker correlation

Influence of Acidic Substances on Compression Deformation Characteristics of Loess

Current research theories on acid-contaminated soils indicate that acids can alter the physical properties of soils, which in turn can affect their engineering mechanical properties. However, compressibility, one of the most important mechanical properties of loess, may be affected by acidic substances. To investigate the influence of acid on the compression properties of loess, this study uses a uniaxial compressor to investigate the changes of compression properties of loess under different acid concentrations and different acid immersion times and attempts to predict the causes of macroscopic compressibility changes from the microscopic pore changes of acid-etched loess. The test results show that when the soaking time is the same, the hydrochloric acid concentration increases from 0 to 3.0 mol/L, the compression coefficient Cc increases by 43.20–87.5%, and the compression yield stress σpc decreases by 51.36–60.86%; when the concentration of hydrochloric acid is the same, the soaking time increases from 1 day to 12 days, the compression coefficient Cc increases by 119.05–197.46%, and compressive yield stress σpc decreases by 10.67–22.02%. The microscopic images of loess soaked for 12 days at 3.0 mol/L hydrochloric acid concentration were compared with those of the original loess. The percentages of micropore, small pore, mesopore, and macropore areas of original loess were 20.90%, 79.10%, 0%, and 0%, respectively. The percentages of micropore, small pore, mesopore, and macropore areas of acid-etched loess were 6.24%, 37.21%, 1.14%, and 55.40%, respectively. The enhancement of the compressive properties of acid-etched loess is the result of the coupling of acid concentration and soaking time, and the change of macroscopic compressive properties may be related to the increase of microscopic macropore area after acid erosion. The results of this study can be used as a reference for the study of soil mechanical properties in acid-contaminated soils

Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world

Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF network machine learning algorithms

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world