Influences of migrant work income on the poverty vulnerability disaster threatened area: A case study of the Three Gorges Reservoir area, China

2017 ◽  
Vol 22 ◽  
pp. 62-70 ◽  
Author(s):  
Dingde Xu ◽  
Li Peng ◽  
Shaoquan Liu ◽  
Chunjiang Su ◽  
Xuxi Wang ◽  
...  
Keyword(s):  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Migrant Work

A correlation of pine wood nematode dispersal rate with human activities-a case study in the Three Gorges Reservoir Area

2017 ◽  
Vol 37 (20) ◽  
Author(s):  
洪承昊 HONG Chenghao ◽  
陈京元 CHEN Jingyuan ◽  
赵勇 ZHAO Yong ◽  
宋德文 SONG Dewen ◽  
陈桂芳 CHEN Guifang ◽  
...  
Keyword(s):  
Human Activities ◽  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Pine Wood Nematode ◽  
Three Gorges ◽  
Dispersal Rate ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges

scholarly journals Landslide Displacement Prediction Combining LSTM and SVR Algorithms: A Case Study of Shengjibao Landslide from the Three Gorges Reservoir Area

2020 ◽  
Vol 10 (21) ◽  
pp. 7830
Author(s):  
Hongwei Jiang ◽  
Yuanyao Li ◽  
Chao Zhou ◽  
Haoyuan Hong ◽  
Thomas Glade ◽  
...  
Keyword(s):  
Neural Networks ◽  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Individual Model ◽  
Optimal Weight ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges

Displacement predictions are essential to landslide early warning systems establishment. Most existing prediction methods are focused on finding an individual model that provides a better result. However, the limitation of generalization that is inherent in all models makes it difficult for an individual model to predict different cases accurately. In this study, a novel coupled method was proposed, combining the long short-term memory (LSTM) neural networks and support vector regression (SVR) algorithm with optimal weight. The Shengjibao landslide in the Three Gorges Reservoir area was taken as a case study. At first, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Single-factor models based on LSTM neural networks and SVR algorithms were used to predict the trend terms of displacement, respectively. Multi-factors LSTM and SVR models were used to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for inputs in the models. Additionally, ensemble models based on the SVR algorithm are used to predict the optimal weight to combine the results of the LSTM and SVR models. The results show that the LSTM models display better performance than SVR models; the ensemble model with optimal weight outperforms other models. The prediction accuracy can be further improved by also considering results from multiple models.

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