SVM-Based Model for Short-Term Rainfall Forecasts at a Local Scale in the Mumbai Urban Area, India

Abstract

In high-density urban areas, flooding affects a large number of people. A rapidly implementable nonstructural measure is the development of an early flood warning mechanism based on observations from ground-based rainfall stations, especially where radars are not yet installed. To increase the lead time for issuing warnings, a reliable short-term rainfall forecasting model is required, specifically for fast-responding urban catchments where the time of concentration is less than 45 min. With this objective, a rainfall forecasting methodology has been developed using the least-squares support vector machine (LS-SVM) and the probabilistic global search–Lausanne (PGSL) technique. The study’s focus was Mumbai, which receives all of its annual rainfall of 2,430 mm during June to September. The Mumbai storm drainage system had been designed to drain