Regarding urban flooding issues, applying Artificial Intelligence (AI) methodologies can provide a timely prediction of imminent incidences of flash floods. The study aims to develop and deploy an effective real-time pluvial flood forecasting AI platform. The platform integrates rainfall hyetographs embedded with uncertainty analyses as well as hydrological and hydraulic modeling. It establishes a large number synthetic of torrential rainfall events and their simulated flooding datasets. The obtained data contain 6000 sets of color-classified rainfall hyetograph maps and 300,000 simulated flooding maps (water depth) in an urban district. The generated datasets are utilized for AI image processing. Through the AI deep learning classifications, the rainfall hyetograph map feature parameters are detected and extracted. The trained features are applied to predict potential rainfall events, recognize their potential inundated water depths as well as display flooding maps in real-time. The performance assessments of the platform are evaluated by Root Means Square Error (RMSE), Nash Sutcliffe Efficiency Coefficient (NSCE) and Mean Absolute Percentage Error (MAPE). The results of RMSE and NSCE indicators illustrate that the methodologies and approaches of the AI platform are reliable and acceptable. However, the values of MAPE show inconsistency. Ultimately, the platform can perform and be utilized promptly in real-time and ensure sufficient lead time in order to prevent possible flooding hazards.

This study aims to model a probabilistic-based reliability analysis, named the RA_IWS_Canal model, for calculating the probability of the irrigation water supply exceeding the water demand (i.e., reliability) within a multi-canal irrigation zone due to variations in hydrological and irrigation uncertainty factors. The proposed RA_IWS_Canal model is developed by coupling uncertainty and risk analysis with a logistic regression equation. The Zhudong irrigation zone, located within the Touqian River watershed in northern Taiwan, was selected as the study area, with the inflow from Shanping Weir, water supplies at 15 irrigation canals, and water intakes of two reservoirs (Baoshan and Baoshan II) and a water treatment plant (Yuandon); 1000 simulations of 10-day irrigation water allocations and resulting exceedance probabilities of the water supplies at the 15 canals were achieved using the multivariate Monte Carlo simulation and the uncertainty with the water allocation model (RIBASIM), and employed in the development of the proposed RA_IWS_Canal model. The model development and application results indicate that the uncertainty factors and the inflow from Shanping Weir markedly and positively influence the exceedance probability of the canal-based irrigation water supply to boost the corresponding reliability (about 0.8). The water intake of the Baoshan Reservoir has a lower relationship (by 0.19) than the Yuandon water treatment plant with the reliabilities of the irrigation water supplies at its downstream canals. As a result, the proposed RA_IWS_Canal model can evaluate the effect of not only the canal-based uncertainty factors, but also the regional features on the irrigation water supply reliability. In addition, using the proposed RA_IWS_Canal model, the planned irrigation water demands at various canals within a multi-canal irrigation zone could be accordingly formulated based on acceptable reliability.

In Taiwan, hydro-meteorological data are fragmented across multiple agencies, limiting the effectiveness of coordinated flood response. To address this challenge and the increasing uncertainty associated with extreme rainfall, a real-time disaster prevention platform has been developed. This system integrates multi-source data and geospatial information through a cluster-based architecture to enhance pluvial flood management. Built on a Service-Oriented Architecture (SOA) and incorporating Internet of Things (IoT) technologies, AI-based convolutional neural networks (CNNs), and 3D drone mapping, the platform enables automated alerts by linking sensor thresholds with real-time environmental data, facilitating synchronized operational responses. Deployed in New Taipei City over the past three years, the system has demonstrably reduced flood risk during severe rainfall events. Region-specific action thresholds and adaptive strategies are continually refined through feedback mechanisms, while integrated spatial and hydrological trend analyses extend the lead time available for emergency response.

Most pier scour monitoring methods cannot be carried out during floods, and data cannot be recorded in real-time. Since scour holes are often refilled by sediment after floods, the maximum scour depth may not be accurately recorded, making it difficult to derive the equilibrium scour depth. This study proposes a novel approach using 16 proximity sensors (VCNL4200), which are low-cost (less than USD 3 each) and low-power (380 µA in standby current mode), to monitor and record the pier scour depth at eight different positions in a flume as it varies with water flow rate. Based on the regression relationship between PS data and distance, the scour trend related to the equilibrium scour depth can be derived. Through the results of 13 local live-bed sediment scour experiments, this PS module was able to record not only the scour depth, but also the development and geometry of the scour under different water flows. Additionally, based on PS data readings, changes in the topography of the scour hole throughout the entire scouring process can be observed and recorded. Since the maximum scour depth can be accurately recorded and the scour trend can be used to estimate the equilibrium scour depth, observations from the experimental results suggest that the critical velocity derived by Melville and Coleman (2000) may have been underestimated. The experimental results have verified that, beyond achieving centimeter-level accuracy, this method also leverages the Internet of Things (IoT) for the long-term real-time observation, measurement, and recording of the formation, changes, and size of scour pits. In addition to further exploring scouring behavior in laboratory studies, this method is feasible and highly promising for future applications in on-site scour monitoring due to its simplicity and low cost. In future on-site applications, it is believed that the safety of bridge piers can be assessed more economically, precisely, and effectively.

Effective mitigation of urban flash floods under extreme rainfalls requires integrated hydrologic monitoring and rapid response mechanisms. The study presents an adaptive flood response framework. It combines real-time rainfall forecasting, CCTV-based flood image classification, drainage network water level monitoring, pumping machinery operations, and automated response controls. The adaptive strategy is structured into three phases to support real-time decision-making: (1) atmospheric sensing and pre-alert actions, (2) subsurface drainage system monitoring and alert activation, and (3) surface run-off detection and response. Over three years of implementation in New Taipei City, the adapted strategy achieved an over 80% success rate in preventing street inundation during intense rainfall events (>25 mm per 10 min). By integrating ensemble modeling, remote sensing, and decision-support tools, the platform transforms climate-induced flood risks into opportunities for resilience.

Urban flooding has occurred in many cities in recent years, causing economic and property losses in severe cases. Taiwan also faces the same risk of urban flooding. This study is divided into two stages. The first stage is to understand the possible causes of drainage bottlenecks in urban areas through urban hydrological and hydrological models, so as to understand the current urban drainage precipitation tolerance capacity to withstand rainwater. The second stage uses real-time and forecast rainfall to forecast water level are calculated by (1) automatic scheduling and calculation of the hydrological and hydraulic models and (2) artificial intelligence combined with big data to predict the water level as a disaster prevention warning and prediction tool. The forecast water levels are calculated for the bottleneck channel section, and then forecasts and other disaster prevention monitoring and early warning are combined with strategies such as early operation of the downstream outlet pumping station of the drainage system to improve the flooding problem in local low-lying areas. This study focuses on the problems faced by Taiwan in urban area drainage improvement projects, such as the complexity of traffic and underground pipelines for people's livelihood. Moreover, extreme rainfall caused by severe convective weather every summer has become one of the main causes of urban flooding. In addition, due to climate change, the hydrological characteristics of urban areas have also changed, such as two consecutive rounds of concentrated rainfall and multi-distributed rainfall. Concentrated rainfall in the region exceeded the drainage design protection standards and caused widespread flooding. In 2024, Taipei City, New Taipei City (located in northern Taiwan), and Kaohsiung City (located in southern Taiwan) all experienced two heavy rains, with rainfall exceeding 80-90 mm per hour, causing serious urban flooding disasters. This indicates that concentrated rainfall in the city makes it difficult for urban rainwater sewer drainage in local low-lying areas of the city to resist such floods. In addition to low-lying areas, it needs to further understand the hydrological disaster prevention in urban areas and which important urban roads are prone to urban flooding due to this type of rainfall, so as to provide disaster prevention strategies. Before the project is improved, the non-engineering rainwater system adaptation and disaster prevention strategies of urban drainage are carried out. According to the characteristics of drainage systems in different sections, suitable strategies are integrated to reduce the frequency of flooding in urban areas and improve the adaptation of urban precipitation tolerance capacity. Strategies such as precipitation tolerance capacity are formulated, and the case of New Taipei City is used to illustrate.