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Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

CaOFeS is a semiconducting oxysulfide with polar layered triangular structure. Here a comprehensive theoretical study has been performed to reveal its physical properties, including magnetism, electronic structure, phase transition, magnetodielectric effect, as well as optical absorption. Our calculations confirm the Ising-like G-type antiferromagnetic ground state driven by the next-nearest neighbor exchanges, which breaks the trigonal symmetry and is responsible for the magnetodielectric effect driven by exchange striction. In addition, a large coefficient of visible light absorption is predicted, which leads to promising photovoltaic effect with the maximum light-to-electricity energy conversion efficiency up to 24.2%.

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