This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.

Typhoon intensity forecast is an important issue. The objective of this study is to construct a 5-day 12-hourly typhoon intensity forecast model based on the adaptive neuro-fuzzy inference systems (ANFIS) to improve the typhoon intensity forecast in the Northwest Pacific. It analyzed the improvement of the ANFIS typhoon intensity forecast model by comparing it with the MLR model when only the atmospheric factor or both atmospheric and oceanic factors are considered. This study collected the SHIPS (Statistical Hurricane Intensity Prediction Scheme) developmental data of typhoons in the Northwest Pacific before landing from 2000 to 2012. The input factors of the ANFIS model were simplified by the stepwise regression procedure (SRP). Subtractive clustering (SC) was used to determine the number of ANFIS rules and to reduce model complexity. Model Index (MI) was taken as the clustering standard of SC to determine the network architecture of the ANFIS typhoon intensity forecast model. The simulated results show that the MI could effectively determine the radius of influence of SC. The typhoon intensity forecast was significantly improved after oceanic environmental factors were added. The improvement of RMSE of ANFIS was the highest at 84 h; the improvement of ANFIS on the underestimated ratio was primarily positive. The Typhoon Songda case study shows that the maximum bias of ANFIS is greatly improved, at 60 h of the lead time, and the improvement percentage of maximum bias is the highest (39%). Overall, the ANFIS model could effectively improve the MLR model in typhoon intensity forecast.