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您的检索式：作者名="Chengguang Lai"
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| 1 | Random packing of tetrahedral particles using the polyhedral discrete element method显示文摘 | Shiwei Zhao Xiaowen Zhou Wenhui Liu Chengguang Lai | 2015 | Particuology2015，13，6： | 7 |
| 2 | Simulation Performance Evaluation and Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D显示文摘Urban floods are becoming increasingly more frequent,which has led to tremendous economic losses.The application of inundation modeling to predict and simulate urban flooding is an effective approach for disaster prevention and risk reduction,while also addressing the uncertainty problem in the model is always a challenging task.In this study,a cellular automaton(CA)-based model combining a storm water management model(SWMM)and a weighted cellular automata 2D inundation model was applied and a physical-based model(LISFLOOD-FP)was also coupled with SWMM for comparison.The simulation performance and the uncertainty factors of the coupled model were systematically discussed.The results show that the CA-based model can achieve sufficient accuracy and higher computational efficiency than can a physical-based model.The resolution of terrain and rainstorm data had a strong influence on the performance of the CA-based model,and the simulations would be less creditable when using the input data with a terrain resolution lower than 15 m and a recorded interval of rainfall greater than 30 min.The roughness value and model type showed limited impacts on the change of inundation depth and occurrence of the peak inundation area.Generally,the CA-based coupled model demonstrated laudable applicability and can be recommended for fast simulation of urban flood episodes.This study also can provide references and implications for reducing uncertainty when constructing a CA-based coupled model. | Zhaoyang Zeng Zhaoli Wang Chengguang Lai | 2022 | International Journal of Disaster Risk Science2022，13，3： | 2 |
| 3 | A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model显示文摘Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance.To improve urban flood prediction efficiency and accuracy,we proposed a framework for fast mapping of urban flood:a coupled model based on physical mechanisms was first constructed,a rainfall-inundation database was generated,and a hybrid flood mapping model was finally proposed using the multi-objective random forest(MORF)method.The results show that the coupled model had good reliability in modelling urban flood,and 48 rainfall-inundation scenarios were then specified.The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events.The spatial inundated depths predicted by the MORF model were close to those of the coupled model,with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951.The MORF model,however,achieved a computational speed of 200 times faster than the coupled model.The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model.Our research provides a novel approach to rapid urban flood mapping and flood early warning. | Yaoxing Liao Zhaoli Wang Chengguang Lai Chong-Yu Xu | 2023 | International Journal of Disaster Risk Science2023，14，2： | 0 |
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