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Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide data integrity, machine-learning (ML) models, and non-landslide sample-selection methods on the accuracy of LSP, taking the Yinghu Lake Basin in Ankang City, Shaanxi Province, as an example. First, previous landslide inventory (totaling 46) and updated landslide inventory (totaling 46 + 176) were established through data collection, remote-sensing interpretation, and field investigation. With the slope unit as the mapping unit, twelve conditioning factors, including elevation, slope, aspect, topographic relief, elevation variation coefficient, slope structure, lithology, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), distance to road, distance to river, and rainfall were selected. Next, the initial landslide susceptibility mapping (LSM) was obtained using the K-means algorithm, and non-landslide samples were determined using two methods: random selection and semi-supervised machine learning (SSML). Finally, the random forest (RF) and artificial neural network (ANN) machine-learning methods were used for modeling. The research results showed the following: (1) The performance of supervised machine learning (SML) (RF, ANN) is generally superior to unsupervised machine learning (USML) (K-means). Specifically, RF in the SML model has the best prediction performance, followed by ANN. (2) The selection method of non-landslide samples has a significant impact on LSP, and the accuracy of the SSML-based non-landslide selection method is controlled by the ratio of the number of landslide samples to the number of mapping units. (3) The quantity of landslides has an impact on how reliably the results of LSM are obtained because fewer landslides result in a smaller sample size for LSM, which deviates from reality. Although the results in this dataset are satisfactory, the zoning results cannot reliably anticipate the recently added landslide data discovered by the interpretation of remote-sensing data and field research. We propose that the landslide inventory can be increased by remote sensing in order to achieve accurate and impartial LSM since the LSM of adequate landslide samples is more reasonable. The research results of this paper will provide a reference basis for uncertain analysis of LSP and regional landslide risk management. [ABSTRACT FROM AUTHOR]

The shift from routine work to nonroutine cognitive work is a key feature of labor markets globally, but there is little evidence on the extent to which tasks differ among workers performing the same jobs in different countries. This paper constructs survey-based measures of routine task intensity (RTI) of jobs consistent with those based on the U.S. O*NET database for workers in 47 countries. It confirms substantial cross-country differences in the content of work within occupations. The extent to which workers' RTI is predicted by technology, supply of skills, globalization, and economic structure is assessed; and their contribution to the variation in RTI across countries is quantified. Technology is by far the most important factor. Supply of skills is next in importance, especially for workers in high-skilled occupations, while globalization is more important than skills for workers in low-skilled occupations. Occupational structure explains only about one-fifth of cross-country variation in RTI. [ABSTRACT FROM AUTHOR]

The accuracy of a debris-flow susceptibility assessment model can not be achieved to an acceptable level without rigorously selecting/processing model input parameters and reliably validating the results with ground truth. In this study, a modified logistic regression model was applied to examine the debris-flow susceptibility in the semi-arid mountainous areas of the southeast Tibetan Plateau, taking the case of the Benzilan-Changbo segment of the Jinsha River. The logistic regression model validation was supported by a field investigation and interpreting aerial photographs. The frequency ratio method was adapted for the sensitivity analysis of ten predisposing factors, including a factor of human activities that has never been considered in previous studies for the selected study area. The model with the highest prediction accuracy (83%) was obtained by comparing 55 possible combinations of the ten factors evaluated using a receiver operating characteristic curve technique. Composite analysis and random sample testing were also conducted to verify the reliability of the susceptibility assessment. The prediction results showed that the actual debris-flow area accounted for 92% of the total predicted debris-flow area. In summary, this study reveals that: (1) the highest susceptibility area was mainly distributed in the northeast and middle riparian zones, partially scattered in the southwest. Human activity was found to be closely related to the probability of debris flow occurrences; (2) under the two situations of independent and index combination participation evaluation, the performances of the same related factors showed largely different effects on the assessment results, which reflected the interrelation and interaction of the factors causing the debris flow; (3) the accuracy of the modified logistic regression model increased by 15%, when compared with the index entropy model in a previous study, and the reasons for this were discussed in the current study. The modified model, along with its enhanced validation process, could be easily extended to other regional mass movement susceptibility analyses. [ABSTRACT FROM AUTHOR]

A large number of the landslide dams located on the major rivers at the southeastern margin of the Tibetan Plateau have been previously identified through remote sensing analysis and field investigations. The Xuelongnang paleolake was one of the lakes formed by these landslide dams in the upper Jinsha River, where the association of a relict landslide dam, lacustrine sediment, and outburst sediment is well preserved. This preservation provides an opportunity to better understand the formation, evolution, and longevity of a large landslide-dammed lake in the upper Jinsha River. It was inferred that the Xuelongnang dammed lake may have been formed by an earthquake-induced paleoavalanche. The surface area of the lake at its peak was estimated at 7.0 × 106 m2, and the corresponding volume was approximately 3.1 × 108 m3. Two outburst flood events were determined to have occurred during the life span of the lake. Based on the 18 ages obtained from optically stimulated luminescence (OSL) and carbon-14 (14C) dating combined with stratigraphic sequences observed in the field, the paleolandslide-dammed lake was formed at approximately 2.1 ka and subsequently breached locally. The dammed lake was sustained for a period of some 900 years based on the chronological constraining. This study confirms that a major landslide-dammed lake can be sustained for at least hundreds of years and breached by several dam breaks in multiple periods, which contributed to the preservation of the knickpoints at millennial scale along the major rivers in the study area. [ABSTRACT FROM AUTHOR]

The Publisher apologizes for this error, which has now been corrected. In the originally published version of this manuscript, captions in Figures 3 and 6 were mistakenly removed during the production process. [Extracted from the article]