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.

Hyperspectral image usually possesses complicated conditions of land-cover distribution, which brings challenge to achieve an effective background representation for hyperspectral anomaly detection. Sparse learning gives a way to overcome this obstacle. A novel sparsity score estimation framework for hyperspectral anomaly detection (SSEAD) is proposed in this paper. Firstly, an overcomplete dictionary and corresponding sparse code matrix are obtained from the hyperspectral data. Then, the frequency of each dictionary atom for reconstruction, which is also called the atom usage probability (AUP), are estimated from the sparse code matrix, from which the strength of each atom for reconstruction is obtained. Finally, the estimated frequencies are transformed to the sparsity score for each pixel. In the proposed detection framework, two operations which aim to enhance the learned dictionary to be more effective for anomaly detection are implemented: 1) dictionary based background feature transformation, and 2) dictionary iterative reweighting. Two real-world hyperspectral datasets are utilized to evaluate the performance of the proposed framework. The experimental results show that the proposed framework achieves superior performance relative to some of the other state-of-the-art anomaly detection methods.