Many urban areas in tropical Southeast Asia, e.g., Bangkok in Thailand, have recently been experiencing unprecedentedly intense flash floods due to climate change. The rapid flood inundation has caused extremely severe damage to urban residents and social infrastructures. In addition, urban Southeast Asia usually has inadequate capacities in drainage systems, complicated land use patterns, and a large vulnerable population in limited urban areas. To reduce the urban flood risk and enhance the resilience of vulnerable urban communities, it has been of essential importance to develop real-time urban flood forecasting systems for flood disaster prevention authorities and the urban public. This paper reviewed the state-of-the-art models of real-time forecasting systems for urban flash floods. The real-time system basically consists of the following subsystems, i.e., rainfall forecasting, drainage system modelling, and inundation area mapping. This paper summarized the recent radar data utilization methods for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. This paper also dealt with available technologies for modelling, e.g., digital surface models (DSMs) for the finer urban terrain of drainage systems. The review indicated that an obstacle to using process-based hydraulic models was the limited computational resources and shorter lead time for real-time forecasting in many urban areas in tropical Southeast Asia. The review further discussed the prospects of data-driven AI models for real-time forecasting systems.

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.

Increasingly frequent, high-intensity rain events associated with climatic change are driving urban drainage systems to function beyond their design discharge capacity. It has become an urgent issue to mitigate the water resource management challenge. To address this problem, a real-time procedure for predicting the inundation risk in an urban drainage system was developed. The real-time procedure consists of three components: (i) the acquisition and forecast of rainfall data; (ii) rainfall-runoff modeling; and (iii) flood inundation mapping. This real-time procedure was applied to a drainage system in the Sukhumvit area of Bangkok, Thailand, to evaluate its prediction efficacy. The results showed precisely that the present real-time procedure had high predictability in terms of both the water level and flood inundation area mapping. It could also determine hazardous areas with a certain amount of lead time in the drainage system of the Sukhumvit area within an hour of rainfall data. These results show the real-time procedure could provide accurate flood risk warning, resulting in more time to implement flood management measures such as pumping and water gate operations, or evacuation.

Plastic waste in river systems represents a major pathway of marine pollution, with rivers estimated to contribute up to 80% of the plastic entering the ocean. This study introduces a deep learning framework with preprocessing for automated detection and tracking of floating plastic waste (macroplastics) in the urban canals of the Chao Phraya River, Thailand. Unlike previous approaches that rely on site-specific retraining or model modification, our method employs a YOLO-based detection model integrated with DeepSORT (Deep Simple Online and Realtime Tracking). The model, initially trained on laboratory flume images, was adapted to real river conditions through a three-step preprocessing pipeline comprising skew correction, background removal, and object region extraction. Experiments on 2000 canal images demonstrated that preprocessing improved the mean Average Precision (mAP) from 0.74 to 0.85, with notable gains for categories such as foam and paper. Testing with a more advanced YOLO architecture further enhanced accuracy, indicating that preprocessing and model upgrades are complementary. These findings suggest that reliable detection and quantification of floating waste can be achieved without retraining. The proposed framework provides a scalable and cost-effective solution for monitoring in data-limited regions, contributing to efforts to mitigate riverine and marine plastic pollution. Future work will address the remaining limitations, as detection performance is still influenced by strong reflections, motion blur, and occlusion, occasionally resulting in missed detections.

Rapid urbanization and persistent air pollution threaten the functional resilience of megacities in Southeast Asia, particularly Bangkok, where PM_2.5 concentrations consistently exceed World Health Organization (WHO) guidelines. To strengthen urban adaptive capacity, this study investigates the role of Nature-based Solutions (NbS), particularly urban green spaces, as resilience-oriented infrastructure for air quality management. Using data from 32 monitoring stations across the Bangkok Metropolitan Administration (BMA) and surrounding areas from 2021 to 2023, spatial and temporal trends in PM_2.5 concentrations were analyzed through geostatistical modeling and inferential statistics. Although all sites exceeded the WHO PM_2.5 guideline of 5 µg/m³, larger and more connected green spaces consistently exhibited better air-quality than the surrounding non-green urban mosaic. Areas with extensive vegetation, greater canopy cover, and more compact park geometries (lower perimeter-to-area ratios) demonstrated improved pollution attenuation capacity, while fragmented parks are more exposed to surrounding emissions. Integration of Local Climate Zone (LCZ) classification further indicated that compact high-rise zones and high-traffic corridors exhibited higher PM_2.5 levels due to reduced airflow and structural confinement. The study underscores the need to embed NbS within resilience-based urban planning to promote long-term environmental stability and public health recovery in rapidly urbanizing megacities like Bangkok.