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The effective assessment of power grid emergency response capability (PERC) is the most fundamental aspect in the emergency continuity plans for urban power grid protection. Existing PERC assessment methods assign PERC indicator values under specific disaster scenarios based on one-round expert scores. However, the emergency continuity plans are often continuous and consider diverse scenarios, which makes the assessment quite time-consuming and unrealistic given too many to consider. In this paper, a case-driven method is proposed to solve the assessment problem of PERC with multiple typhoon disaster scenarios while considering the past experience as a guide for real decision making. Firstly, in terms of hierarchical PERC indicator framework, the framework structure of power grid emergency cases is proposed to make case experience modeled and reusable. Secondly, a case scenario similarity algorithm is applied to select the PERC assessment rules (by mapping relations between response indicators and assessment results) that are most similar to the real scenarios. Then the rules are integrated while considering rule variety and case weights. Next, the hierarchical assessment results are obtained by integrating the confidence coefficient values and corresponding utility values. Finally, the method is verified based on the empirical data from Shenzhen, China. The results show the proposed method not only helps in integrating case experience but also assists in quickly generating reasonable PERC assessment results. • Power grid emergency continuity plan requires considering disaster scenario diversity. • Existing capability assessment methods are limited by unsustainable expert scoring. • We use a case-driven scheme to assess emergency capability through experience reuse. • Capability assessment rules are extracted by measuring case scenario similarity. • A three-level indicator framework is constructed for emergency capability assessment. [ABSTRACT FROM AUTHOR]

• Machine learning (ML) models can determine the rainfall flooding threshold as a line. projected in a plane spanned by two principal components, thereby providing a binary result (flood or no flood). • Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, greatly raise the accuracy (ACC) to 96.5% and lowering the false alert rate to 25%. • Rainfall threshold based flood prediction can be executed rapidly and simply, this method allows decision makers time for a high-level assessment of flood risk, providing valuable lead time for citizens in the flood-prone areas to be warned. Urban pluvial flooding is a threatening natural hazard in urban areas all over the world, especially in recent years given its increasing frequency of occurrence. In order to prevent flood occurrence and mitigate the subsequent aftermath, urban water managers aim to predict precipitation characteristics, including peak intensity, arrival time and duration, so that they can further warn inhabitants in risky areas and take emergency actions when forecasting a pluvial flood. Previous studies that dealt with the prediction of urban pluvial flooding are mainly based on hydrological or hydraulic models, requiring a large volume of data for simulation accuracy. These methods are computationally expensive. Using a rainfall threshold to predict flooding based on a data-driven approach can decrease the computational complexity to a great extent. In order to prepare cities for frequent pluvial flood events – especially in the future climate – this paper uses a rainfall threshold for classifying flood vs. non-flood events, based on machine learning (ML) approaches, applied to a case study of Shenzhen city in China. In doing so, ML models can determine several rainfall threshold lines projected in a plane spanned by two principal components, which provides a binary result (flood or no flood). Compared to the conventional critical rainfall curve, the proposed models, especially the subspace discriminant analysis, can classify flooding and non-flooding by different combinations of multiple-resolution rainfall intensities, greatly raising the accuracy to 96.5% and lowering the false alert rate to 25%. Compared to the conventional model, the critical indices of accuracy and true positive rate (TPR) were 5%-15% higher in ML models. Such models are applicable to other urban catchments as well. The results are expected to be used to assist early warning systems and provide rational information for contingency and emergency planning. [ABSTRACT FROM AUTHOR]