| 1 | Dynamic prediction of landslide life expectancy using ensemble system incorporating classical prediction models and machine learning显示文摘With the development of landslide monitoring system,many attempts have been made to predict landslide failure-time utilizing monitoring data of displacements.Classical models(e.g.,Verhulst,GM(1,1),and Saito models)that consider the characteristics of landslide displacement to determine the failuretime have been investigated extensively.In practice,monitoring is continuously implemented with monitoring data-set updated,meaning that the predicted landslide life expectancy(i.e.,the lag between the predicted failure-time and time node at each instant of conducting the prediction)should be re-evaluated with time.This manner is termed“dynamic prediction”.However,the performances of the classical models have not been discussed in the context of the dynamic prediction yet.In this study,such performances are investigated firstly,and disadvantages of the classical models are then reported,incorporating the monitoring data from four real landslides.Subsequently,a more qualified ensemble model is proposed,where the individual classical models are integrated by machine learning(ML)-based meta-model.To evaluate the quality of the models under the dynamic prediction,a novel indicator termed“discredit index(b)”is proposed,and a higher value of b indicates lower prediction quality.It is found that Verhulst and Saito models would produce predicted results with significantly higher b,while GM(1,1)model would indicate results with the highest mean absolute error.Meanwhile,the ensemble models are found to be more accurate and qualified than the classical models.Here,the performance of decision tree regression-based ensemble model is the best among the various ML-based ensemble models. | Lei-Lei Liu Hao-Dong Yin Ting Xiao Lei Huang Yung-Ming Cheng | 2024 | Geoscience Frontiers2024，15，2： | 0 |
| 2 | Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections显示文摘Extreme precipitation-induced landslide events are projected to increase under climate change,which poses a serious threat to human lives and property.In this study,a global-scale landslide risk assessment model was established using global landslide data,by considering landslide hazard,exposure,and vulnerability.The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes.The results show that compared to the 30-year period from 1971 to 2000,the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7%and 10%,respectively,in the future 30-year periods of 2031–2060 and 2066–2095.The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380,respectively(with growth rates of 140%and 160%),during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario.The top 10 countries with the highest casualty risk of landslides are China,Afghanistan,India,the Philippines,Indonesia,Rwanda,Turkey,Nepal,Guatemala,and Brazil,60%of which are located in Asia.The frequency and intensity of extreme precipitation will increase under climate change,which will lead to an increase in casualties from landslides in mountainous areas globally,and this risk should be taken seriously.The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change,which still has uncertainty in terms of outcomes,and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk. | Xia Wang Ying Wang Qigen Lin Xudong Yang | 2023 | International Journal of Disaster Risk Science2023，14，5： | 0 |
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