The"9.5"Luding earthquake(Ms 6.8),which occurred on September 5,2022,has triggered thousands of landslides,and caused coseismic landslide sediment in the mountain basin to increase significantly.After the Lu...The"9.5"Luding earthquake(Ms 6.8),which occurred on September 5,2022,has triggered thousands of landslides,and caused coseismic landslide sediment in the mountain basin to increase significantly.After the Luding earthquake,landslide sediment may continue to divert to channels,and increase the activity of debris flows.Importantly,the formation of debris flows can pose a major threat to infrastructure,lives and property.To better understand the landslide sediment that increased by the"9.5"Luding earthquake and its impact on the activity of debris flows,we mapped the coseismic landslide database using satellite images.A total of9142 landslides with an area of 49.51 km^(2),covering4.81%of the whole basin,were triggered by the Luding earthquake.The coseismic landslides induced by this earthquake are dominated by shallow landslides and are densely distributed in the combined zone of the Xianshuihe fault and the Daduhe fault.Approximately 333.31×10^(6)m^(3)(error:111.43×10^(6)m^(3)/-70.73×10^(6)m^(3))of coseismic landslide sediments were induced by the earthquake in the epicenter,and the landslide materials were concentrated downstream of the basins.In addition.more than 13986.45×10^(4)m^(3)(error:4675.67×10^(4)m^(3)/-2967.92×10^(4)m^(3))of landslide sediment may supply for debris flow occurrence.Simultaneously,the small basins that are distributed near Moxi,Detuo and the junction of the Xianshuihe fault and Daduhe fault are more susceptible to debris flows when rainstorms hit these regions.Therefore,prevention and mitigation measures,early warning,and land use planning should be adopted in advance in these regions.However,from the perspectives of landslide scale and the degree of landslide-channel coupling,the activity or active intensity of debris flows in the Luding earthquake area may be lower than that in the epicenter area of the 2008 Wenchuan earthquake.展开更多
The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple pr...The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.U21A2008)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0902)+1 种基金Science and Technology Project of Tibet Autonomous Region(Grant No.XZ202101ZD0001G)CAS Light of West China Program。
文摘The"9.5"Luding earthquake(Ms 6.8),which occurred on September 5,2022,has triggered thousands of landslides,and caused coseismic landslide sediment in the mountain basin to increase significantly.After the Luding earthquake,landslide sediment may continue to divert to channels,and increase the activity of debris flows.Importantly,the formation of debris flows can pose a major threat to infrastructure,lives and property.To better understand the landslide sediment that increased by the"9.5"Luding earthquake and its impact on the activity of debris flows,we mapped the coseismic landslide database using satellite images.A total of9142 landslides with an area of 49.51 km^(2),covering4.81%of the whole basin,were triggered by the Luding earthquake.The coseismic landslides induced by this earthquake are dominated by shallow landslides and are densely distributed in the combined zone of the Xianshuihe fault and the Daduhe fault.Approximately 333.31×10^(6)m^(3)(error:111.43×10^(6)m^(3)/-70.73×10^(6)m^(3))of coseismic landslide sediments were induced by the earthquake in the epicenter,and the landslide materials were concentrated downstream of the basins.In addition.more than 13986.45×10^(4)m^(3)(error:4675.67×10^(4)m^(3)/-2967.92×10^(4)m^(3))of landslide sediment may supply for debris flow occurrence.Simultaneously,the small basins that are distributed near Moxi,Detuo and the junction of the Xianshuihe fault and Daduhe fault are more susceptible to debris flows when rainstorms hit these regions.Therefore,prevention and mitigation measures,early warning,and land use planning should be adopted in advance in these regions.However,from the perspectives of landslide scale and the degree of landslide-channel coupling,the activity or active intensity of debris flows in the Luding earthquake area may be lower than that in the epicenter area of the 2008 Wenchuan earthquake.
文摘The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.
