An efficient inundation model is necessary for emergency flood responses during storm events. Cellular automata (CA)-based flood models have been proven to produce rapid results while maintaining a certain degree of accuracy. However, the need for computational resources dramatically increases when the number of grid cells increases. Digital elevation model (DEM)-based models generate results even faster, but the simplified governing equations within the models fail to reflect temporal flood evolution. To achieve rapid flood modeling while maintaining model simplicity, a novel two-dimensional hybrid inundation model (HIM) was developed by combining the CA- and DEM-based concepts. Given the temporal flood evolution generated by the CA concept, final finer-scale predictions were obtained by applying the DEM-based concept. The performance of this model was compared to those of widely used, physically based hydraulic models using three UK Environment Agency (EA) benchmark test cases. The HIM yielded consistent prediction results but was faster than the CA-based model. Finally, a comparison was made against flood observations, and the overall root mean squared error (RMSE) for flood depth was 0.388–0.400 m. Considering the uncertainty in the observed flood depths, the HIM shows promising potential to serve as an intermediate tool for emergency response in practical cases.

Extreme weather events cause stream overflow and lead to urban inundation. In this study, a decentralized flood monitoring system is proposed to provide water level predictions in streams three hours ahead. The customized sensor in the system measures the water levels and implements edge computing to produce future water levels. It is very different from traditional centralized monitoring systems and considered an innovation in the field. In edge computing, traditional physics-based algorithms are not computationally efficient if microprocessors are used in sensors. A correlation analysis was performed to identify key factors that influence the variations in the water level forecasts. For example, the second-order difference in the water level is considered to represent the acceleration or deacceleration of a water level rise. According to different input factors, three artificial neural network (ANN) models were developed. Four streams or canals were selected to test and evaluate the performance of the models. One case was used for model training and testing, and the others were used for model validation. The results demonstrated that the ANN model with the second-order water level difference as an input factor outperformed the other ANN models in terms of RMSE. The customized microprocessor-based sensor with an embedded ANN algorithm can be adopted to improve edge computing capabilities and support emergency response and decision making.

Accurate precipitation estimation is essential for hydrological studies, particularly in regions with complex climatic and topographical characteristics, such as Greater Bandung, Indonesia. This study evaluates the performance of two high-resolution global precipitation datasets (GPDs), ERA5 and PERSIANN-CDR, by comparing them against observations from 32 rainfall stations at daily and monthly scales. The evaluation employs a straightforward ranking-based system considering three objective functions: Root Mean Square Error (RMSE), bias (B), and Pearson correlation coefficient (R). The results reveal that ERA5 performs better than PERSIANN-CDR for daily precipitation, as indicated by higher ranking scores across the selected metrics. Interestingly, the opposite is true for monthly precipitation, where PERSIANN-CDR outperforms ERA5. However, while the ranking system offers a practical comparative framework, the objective function values for both datasets exhibit considerable deviations from ground observations. Daily RMSE values reach as high as 24.89 mm, and monthly RMSE peaks at 525.605 mm, alongside less satisfactory bias and correlation scores. These findings suggest that while the ranking system helps identify the more suitable GPD for specific temporal scales, neither dataset is immediately fit for hydrological applications without further refinement. Bias correction of the GPDs against ground-based observations is recommended to improve their reliability and usability in hydrological analyses. This study highlights the need for tailored adjustments to GPDs in regions with high climatic variability.

A robust 2D inundation model is needed to support flood warning systems in urban areas. The conventional 2D hydrodynamic model uses shallow water equations as the governing equation and is computationally expensive. Although the models have benefited from parallel computation techniques, some issues remain. As an alternative, many flood models have been developed using different approaches, such as Cellular Automata (CA), DEM-based (DBM), and data-driven models. The hybrid inundation model (HIM) was developed by combining the CA-DBM concepts. The purpose of this study is to implement the parallel computation technique to increase the efficiency of HIM. The model performance was evaluated using the historical flood event in Chiayi County, Taiwan. Results showed that there is no significant difference between HIM and TUFLOW in terms of flood depth estimation, even though TUFLOW included the drainage system within the analysis. These results proved that the drainage system was not working during the event. HIM and TUFLOW give underestimated flood depth prediction compared to the observed data. The main reason because the observed data was obtained from local community testimonies. Hence, there might be many uncertainties in the observed data value. Finally, the parallelization process successfully decreased the computation time of the original HIM. The computation was decreased from 450 to 11 minutes depending on the number of cores used in the simulation.

Monitoring urban pluvial floods remains a challenge, particularly in dense city environments where drainage overflows are localized, and sensor-based systems are often impractical. Physical sensors can be costly, prone to theft, and difficult to maintain in areas with high human activity. To address this, we developed an innovative flood detection framework that utilizes publicly accessible CCTV imagery and large language models (LLMs) to classify flooding conditions directly from images using natural language prompts. The system was tested in Bandung, Indonesia, across 340 CCTV locations over a one-year period. Four multimodal LLMs, ChatGPT-4.1, Gemini 2.5 Pro, Mistral Pixtral, and DeepSeek-VL Janus, were evaluated based on classification accuracy and operational cost. ChatGPT-4.1 achieved the highest overall accuracy at 85%, with higher performance during the daytime (89%) and lower accuracy at night (78%). A cost analysis showed that deploying GPT-4.1 every 15 min across all locations would require approximately USD 59,568 per year. However, using compact models like GPT-4 nano could reduce costs by up to seven times, with minimal loss of accuracy. These results highlight the trade-off between performance and affordability, especially in developing regions. This approach offers a scalable, passive flood monitoring solution that can be integrated into early warning systems. Future improvements may include multi-frame image analysis, automated confidence filtering, and multi-level flood classification for enhanced situational awareness.

A landslide triggered by heavy rainfall occurred in the Al-Husainiyyah Junior High School area on April 26th, 2023. The landslide damaged the nearby access roads, the existing retaining wall, and a futsal court threatening the safety of the teachers and students in conducting the learning activities that especially require open areas. Thus, this community service aims to provide a cantilever wall design with geofoam as an innovative lightweight backfill material to reinforce the slope and repair the damaged structures. Field surveying and numerical analysis were then conducted to understand the current slope and soil conditions and to analyze the stability of the slope and retaining wall. The results show that a cantilever wall with a total height of 1.8 m supported by a strauss pile row with a diameter of 0.4 m, 4 m in length, and 2.5 m in spacing could increase the stability of the slope.