Mountain torrent disasters are common natural hazards that occur in mountainous terrains. The risk assessment of mountain torrent disasters entails a multi-criteria decision-making process and involves a transformation between qualitative and quantitative uncertainties. By incorporating probability statistics and fuzzy set theories, cloud model derived from information science, can aid the required transformation between the qualitative concepts and quantitative data. A cloud model-based approach has thus been proposed for practical risk assessment of mountain torrent disasters. The hybrid weighting method comprising the analytic hierarchy process (AHP) and entropy was employed for determining the weights of the indicators in the multi-criteria decision-making process. The degree of certainty associated with a particular risk level can be calculated through repeated assessments by employing the normal cloud model. The proposed approach was validated by comparing with the actual situation. The obtained results demonstrate that the cloud-model-based method is capable of indicating the risk level of mountain torrent disasters, as well as signifying the relative probability of risk at the same level. The proposed study provides guidelines for future risk management of basin floods and extends the scope of present risk-evaluation methods. Thus, the proposed study can be helpful in the precaution of mountain torrent hazards.

Dammed lakes are an important secondary hazard caused by earthquakes. They can induce further damage to nearby humans. Current hydrology calculation research on dammed lakes usually lacks spatial expressive ability and cannot accurately conduct impact assessment without the support of remote sensing, which obtains important characteristic information of dammed lakes. The current study aims to address the issues of the potential impact area estimate of earthquake-induced dammed lakes by combining remote sensing (RS), a geographic information system (GIS), and hydrological modeling. The Tangjiashan dammed lake induced by the Wenchuan earthquake was selected as the case for study. The elevation-versus-reservoir capacity curve was first calculated using the seed-growing algorithm based on digital elevation model (DEM) data. The simulated annealing algorithm was applied to train the hydrological modeling parameters according to the historical hydrologic data. Then, the downstream water elevation variational process under different collapse capacity conditions was performed based on the obtained parameters. Finally, the downstream potential impact area was estimated by the highest water elevation values at different hydrologic sections. Results show that a flood with a collapse elevation of at least 680 m will impact the entire downstream region of Beichuan town. We conclude that spatial information technology combined with hydrological modeling can accurately predict and demonstrate the potential impact area with limited data resources. This paper provides a better guide for future immediate responses to dammed lake hazard mitigation.

The research in this paper is to recognize and locate sand dredges in Changjiang River based on ASAR remote sensing data. Chenglingji is selected to be experimental area. Adopting advanced radar remote sensing image can satisfy the requirement to monitor and locate sand dredges in Changjiang River ignoring the effect of weather condition and night limit. The program is developed with the IDL which property is a visual language facing matrix. Some key technologies in this paper, such as filtering and noise reduction, the process of image based on morphology, edge detection, clustering, extraction of ship's shape feature and so on, are researched. Comparing with the information gathering from land monitoring station, this method can achieve satisfied accurateness.