Owing to their short duration and high intensity, flash floods are among the most devastating natural disasters in metropolises. The existing warning tools—flood potential maps and two-dimensional numerical models—are disadvantaged by time-consuming computation and complex model calibration. This study develops a data-driven, probabilistic rainfall-inundation model for flash-flood warnings. Applying a modified support vector machine (SVM) to limited flood information, the model provides probabilistic outputs, which are superior to the Boolean functions of the traditional rainfall-flood threshold method. The probabilistic SVM-based model is based on a data preprocessing framework that identifies the expected durations of hazardous rainfalls via rainfall pattern analysis, ensuring satisfactory training data, and optimal rainfall thresholds for validating the input/output data. The proposed model was implemented in 12 flash-flooded districts of the Xindian River. It was found that (1) hydrological rainfall pattern analysis improves the hazardous event identification (used for configuring the input layer of the SVM); (2) brief hazardous events are more critical than longer-lasting events; and (3) the SVM model exports the probability of flash flooding 1 to 3 h in advance.

Large-scaled disaster events had increasingly occurred worldwide due to global and environmental change. Evidently, disaster response cannot rely merely on the public force. In the golden hour of crisis, not only the individuals should learn to react, protect themselves, and try to help each other, but also the local school, enterprise, non-government organization (NGO), nonprofit organization (NPO), and volunteer groups should collaborate to effectively deal with disaster events. New Taipei City (NTPC), Taiwan, was aware of the need for non-public force response and therefore developed the process of enhancing local disaster management networks through promoting the resilient community since 2009. The concept of a resilient community is to build community-based capacity for mitigation, preparedness, response, and recovery in an all-hazards manner. This study organized the NTPC experience and presented the standard operation procedure (SOP) to promote the resilient community, key obstacles, maintenance mechanism, and the successful formulation of the local disaster management network. The performance of the promotion was evaluated through a questionnaire survey and found that participants affirmed the positive effect of building community capacity through the entire process. In general, the resilient community as the center of the local disaster management work is shown promising to holistically bridge the inner/outer resources and systematically respond to disaster events.

Pluvial floods are the most frequent natural hazard impacting urban cities because of extreme rainfall intensity within short duration. Owing to the complex interaction between rainfall, drainage systems and overland flow, pluvial flood warning poses a challenge for many metropolises. Although physical-based flood inundation models could identify inundated locations, hydrodynamic modeling is limited in terms of computational costs and sophisticated calibration. Thus, herein, a quick pluvial flood warning system using rainfall thresholds for central Taipei is developed. A tabu search algorithm is implemented with hydrological-analysis-based initial boundary conditions to optimize rainfall thresholds. Furthermore, a cross test is adopted to evaluate the effect of each rainfall event on rainfall threshold optimization. Urban sewer flood is simulated via hydrodynamic modeling with calibration using crowdsourced data. The locations and time of occurrence of pluvial floods can be obtained to increase the quality of observed data that dominate the accuracy of pluvial flood warning when using rainfall thresholds. The optimization process is a tabu search based on flood reports and observed data for six flood-prone districts in central Taipei. The results show that optimum rainfall thresholds can be efficiently determined through tabu search and the accuracy of the issued flood warnings can be significantly improved.