A Data-Driven Probabilistic Rainfall-Inundation Model for Flash-Flood Warnings

Abstract

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. Such a combined model is quick and suitable for fluvial floods, but inapplicable to pluvial flash floods. A similar combined hydrological model, which derives the rainfall thresholds in pluvial flash floods, was considered by Forestieri et al. (2016) [5]. In natural hazards induced by short-term heavy rainfall such as flash floods or landslides, a timely warning is more important than evaluating the disaster impacts on people. Therefore, the empirical rainfall thresholds of flash floods and rainfall-induced landslides have been increasingly sought in recent years [5][6][7][8][9][10][11][12][13][14][15].The local governments in Taiwan also consider the rainfall thresholds in flash floods and landslides [16][17][18]. In the official flash-flood warning system of Taiwan, the rainfall thresholds in all counties and districts are decided from five cumulative rainfalls of durations 1, 3, 6, 12, and 24 h. At least one rain gauge is assigned as the reference rain gauge for each county or district [19]. In 2017, the Water Resources Agency (WRA) used 754 sets of rainfall thresholds based on 500 rain gauges and warned 368 counties and districts of pending flash floods in Taiwan. However, the rainfall thresholds are based on historical flood records, which lack the parameters (start times, areas, and durations of the floods) required in complex flood warning models. Most of the historical flood records in Taiwan are reports of people's phone calls, which are checked by officers several hours later. Therefore, when determining rainfall thresholds, the historical flood records of the WRA are useful only for predicting whether a rainfall event will cause a disaster. Although potential inundation maps improve the information input [18,20,21], the models based on th...

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

“…In recent years, ML techniques have been increasingly employed in infrastructure risk management, including optimized infrastructure maintenance strategies detection (Yao, Dong, Jiang, & Ni, 2020), failures detection in buildings (Rafiei & Adeli, 2017c) and infrastructure networks (M. Wang & Cheng, 2020), safety of structures and infrastructures monitoring (Rafiei & Adeli, 2018), and postdisaster damage and loss estimation (Pan, Lin, & Liao, 2019). In particular, ML techniques have shown promising results in infrastructure risk assessment.…”

“…For example, Ghaderi, Motamedvaziri, Vafakhah, and Dehghani (2019) used ML techniques such as support vector machine (SVM) and genetic expression programming (GEP) for flood frequency analysis to improve flood risk management. SVM has also been used to improve flood warning systems by developing a probabilistic rainfall inundation model (Pan et al., 2019) and to assess flood risks (Opella & Hernandez, 2019). Moreover, modular local support vector regression (SVR) models and local ANN models have been employed to predict rainfall level (Wu & Chau, 2013).…”

A General Overview of the Risk-Reduction Strategies for Floods and Droughts

“…The model itself may not include the abovementioned governing equations but instead involves mathematical equations from the analysis of time series data. The analysis can then be used to provide flood predictions and warnings e.g., [69,70]. The quantity and quality of data have a significant impact on the performance.…”

“…Because of rapid development in data science, the artificial intelligent or data-driven models are getting most researchers' attention if observation data is provided. Among all models, Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are most commonly used [69][70][71].…”

“…Prediction and Warning Flood Physics-based models [53][54][55][56][57][58][59][60][61] Data-driven models [69][70][71] Other alternatives: rainfall threshold/index-based models [72][73][74]…”