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.

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.

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.

Disaster prevention IoT monitoring technology can be used to solve some problems in urban disaster prevention. For example, in the past, urban areas often experienced extreme regional rainstorms, which caused flooding, traffic chaos, and emergency response time and insufficient support for disaster prevention personnel. Especially during commuting hours, the government has difficulty in flooding and traffic management. This research is to use the disaster prevention Internet of Things monitoring technology to investigate the causes of flooding in urban flood-prone areas, monitor network planning, and install monitoring equipment. Through the storm sewer system monitoring network, set the warning water level value in the sewer system, transmit the water level information in real-time, and determine whether the system downstream pump station can be started to pump in advance to reduce the water level of the storm sewer system and the occurrence of flooding. In areas where there is no sewer system, the pavement flooding sensor is installed to monitor the flooding situation on the land surface. When the land surface is flooded, it is necessary to add regional forecast rainfall information to determine whether it will affect regional traffic. Traffic instructions for no-entry areas. Other real-time information about rivers, regional drainage water level stations, and rainfall stations are the basis for decision-making. Finally, urban storm sewer monitoring and management platforms are built to provide real-time information and a grasp of possible disasters. Take New Taipei City, Taiwan as an example to carry out research on the integration of water conservancy information.

Climate change has gradually affected Taiwan's agricultural environment. The number of raining days has decreased, the rainfall intensity has increased, and the drought time has been prolonged. In addition, with the mountainous terrain of Taiwan, rainfall is not easy to be stored and used. The summer and autumn are rainy seasons, which are prone to flooding disasters, the lack of water in spring and winter causes droughts that cause insufficient agricultural water supply, and the stable supply of food is closely related to people's livelihood needs. Therefore, the research department uses UAV imaging technology to identify agricultural crops, grasp the agricultural crops and water supply needs in the spring and winter seasons, and try to estimate the water demand and distribution water volume as the management basis of agricultural irrigation and drainage. Use long-term meteorological models to estimate rainfall results in the next month, and determine whether there are water shortage characteristics in agricultural crop areas. If there is a water shortage, further use the Internet of Things monitoring technology to monitor the inflow and outflow of agricultural crops in irrigated areas, control and distribute the required water consumption, and then to reduce water supply at night or supplying irrigation water in turn in response to the water shortage during the irrigation period. In the summer and autumn rainfall periods, the Internet of Things technology is also used to observe the water level and flow discharge of the main irrigation waterways, and set the rainfall and water level early warning values to reduce the occurrence of flooding disasters in agricultural areas, and use the immediate hydrological and hydraulic models to forecast the future suggestions such as hourly water level and flow discharge and gate control provide timely information on agricultural disaster warnings. The relevant research area is in the Meinong Agricultural Area (about 4,000 ha) of Farm Irrigation Association of Kaohsiung Taiwan as an example. Through web pages hourly displays Internet of Things information, model analysis results, and disaster prevention early warning results to let the management units understand the actual status of agricultural irrigation areas.