PSO-SVR Rainfall Forecast-Assisted Real-time Optimal Operation of Urban Drainage Systems

Abstract

One of the most effective parameters in hydrologic modeling and water resource management issues such as flood warning and real time control of urban drainage systems is short term rainfall forecasting accuracy [1]. In recent decades, developing real-time urban flood forecasting models has been investigated through various studies and different machine and deep learning models have been applied as a prediction model [2]. For instance, in [3], the role of short-term rainfall forecasts in predictive real-time optimal operation (PRTOP), for five adaptive PRTOP models, have been compared.In this study, based on the result of the [1], one of the superior rainfall forecasting models, the machine learning-based particle swarm optimization (PSO)-support vector regression (SVR) rainfall forecasting approach, is used to develop a 15-minute ahead forecast model of rainfall depth. The PSO-SVR model is linked then to the harmony search (HS)-storm water management model (SWMM) as an optimization-simulation model in a predictive real-time operation model to minimize the flood volume, objective function = min (flood volume), at the control point of a portion of an urban drainage system in Tehran, Iran. Subsequently, the effect of integrating forecasting model with the simulation-optimization model (HS-SWMM) has been examined.The application of the proposed real-time operation approach through optimizing the operation of the system for eight selected rainfall events, each of them has been selected from different classes, reveals its outperformance over a reactive real-time operation model (RTOP) by decreasing the flood volume at the control point up to 7.5%. Keywords: Urban Drainage Systems, Short-term Rainfall Forecasting, Real-time Operation, Machine Learning. [1] Adaryani, F. R., Mousavi, S. J., & Jafari, F. (2022). Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN. Journal of Hydrology, 614, 128463.[2] Piadeh, F., Behzadian, K., & Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 127476.[3] Jafari, F., Mousavi, S. J., & Kim, J. H. (2020). Investigation of rainfall forecast system characteristics in real-time optimal operation of urban drainage systems. Water Resources Management, 34(5), 1773-1787.