Lulu Ren
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Lulu Ren has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Bairu Chen
Research Institute of Engineering Safety Science, PowerChina Guiyang Engineering Corporation Limited, Guiyang 550081, China
1 shared publication - Feixiang Li
Ocean College, Zhejiang University, Zhoushan 316021, China
1 shared publication - Guanqiong Ye
Ocean College, Zhejiang University, Zhoushan, China
1 shared publication - Qian Chen
Ocean College, Zhejiang University, Zhoushan 316021, China
1 shared publication - Xuchao Yang
Ocean College, Zhejiang University, Zhoushan 316021, China
1 shared publication
Latest Publications
- Open Access
- 24 January 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (3) , 689
China’s Wealth Capital Stock Mapping via Machine Learning Methods
The frequent occurrence of extreme weather and the development of urbanization have led to the continuously worsening climate-related disaster losses. Socioeconomic exposure is crucial in disaster risk assessment. Social assets at risk mainly include the buildings, the machinery and the equipment, and the infrastructure. In this study, the wealth capital stock (WKS) was selected as an indicator for measuring social wealth. However, the existing WKS estimates have not been gridded accurately, thereby limiting further disaster assessment. Hence, the multisource remote sensing and the POI data were used to disaggregate the 2012 prefecture-level WKS data into 1000 m × 1000 m grids. Subsequently, ensemble models were built via the stacking method. The performance of the ensemble models was verified by evaluating and comparing the three base models with the stacking model. The stacking model attained more robust prediction results (RMSE = 0.34, R2 = 0.9025), and its prediction spatially presented a realistic asset distribution. The 1000 m × 1000 m WKS gridded data produced by this research offer a more reasonable and accurate socioeconomic exposure map compared with existing ones, thereby providing an important bibliography for disaster assessment. This study may also be adopted by the ensemble learning models in refining the spatialization of the socioeconomic data.
- 24 January 2023
- Published by MDPI AG in Remote Sensing
- Vol. 15 (3) , 689
Open AccessChina’s Wealth Capital Stock Mapping via Machine Learning Methods
The frequent occurrence of extreme weather and the development of urbanization have led to the continuously worsening climate-related disaster losses. Socioeconomic exposure is crucial in disaster risk assessment. Social assets at risk mainly include the buildings, the machinery and the equipment, and the infrastructure. In this study, the wealth capital stock (WKS) was selected as an indicator for measuring social wealth. However, the existing WKS estimates have not been gridded accurately, thereby limiting further disaster assessment. Hence, the multisource remote sensing and the POI data were used to disaggregate the 2012 prefecture-level WKS data into 1000 m × 1000 m grids. Subsequently, ensemble models were built via the stacking method. The performance of the ensemble models was verified by evaluating and comparing the three base models with the stacking model. The stacking model attained more robust prediction results (RMSE = 0.34, R2 = 0.9025), and its prediction spatially presented a realistic asset distribution. The 1000 m × 1000 m WKS gridded data produced by this research offer a more reasonable and accurate socioeconomic exposure map compared with existing ones, thereby providing an important bibliography for disaster assessment. This study may also be adopted by the ensemble learning models in refining the spatialization of the socioeconomic data.