A model involving meteorological factors for short-to-medium term water level prediction of small- and medium-sized urban rivers

Abstract With the increasing of extreme weathers, cities, especially the small- and medium-sized urban rivers with the protection areas less than 200 square hectares, are experiencing significantly more flood disasters worldwide. Heavy snowfalls and rainfalls can rapidly overflow these rivers and cause floods due to the their unique geographic locations and fast runoff and confluence. Therefore, it is particularly important to accurately predict the short-to-medium term water levels of such rivers for reducing and avoiding urban floods. In the present work, a particle swarm optimization (PSO)-support vector machine (SVM) water level predication model was constructed by combining PSO and SVM and trained with the meteorological data of Wuhan, China, and the water level data of Yangtze River. The PSO-SVM model is able to lower mean square error (MSE) 70.47% and increase coefficient of determination (R2) 7.02% of the prediction results, as compared with SVM model alone. The highly accurate PSO-SVM model can be used to predict river water level real-time using the hourly weather and water level data, which thereby provides quantitative data support for urban flood control, construction management of water projects, improving response efficiency and reducing safety risks.