Rapid urbanisation and climate change-induced watershed hydrologic alteration have increased urban flooding risks. As a strategy for urban flood control, low impact development (LID) practices focus on promoting the infiltration and detention of stormwater runoff. A modelling-based study was conducted to analyse LID performance in urban flooding reduction. Two-dimensional simulations were conducted to visualise the flood risk under different precipitation scenarios and LID schemes. An integrated evaluation system based on life-cycle cost analysis, the analytic hierarchy process, and regret decision theory were applied to assess the LID performance in multiple aspects. The results indicated that the LID practices achieved runoff control and flood mitigation to some extent, especially under mild rainfall events. According to the integrated evaluation, urban flooding risk could be sufficiently reduced with the LID combination of bioretention, infiltration trenches, and rain barrels in the given area.
Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms.