Real-Time Flood Forecasting using Satellite Precipitation Product and Machine Learning Approach in Bagmati River Basin, India

Abstract

Abstract Real-time flood forecasting (RTFF) is crucial for early flood warnings. It relies on real-time hydrological and meteorological data. Satellite Precipitation Products (SPPs) offer real-time global precipitation estimates and have emerged as a suitable option for rainfall input in RTFF models. This study first compared the daily SPP data of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) with observed rainfall data of Indian Meteorological Department (IMD) from the year 2001 to 2009 using contingency tests. Hourly rainfall from this SPP is used to build four RTFF models based on machine learning: feedforward neural network (FFNN), extreme learning machine (ELM), wavelet-based feedforward neural network (W-FFNN), and wavelet-based extreme learning machine (W-ELM). These models have been trained and tested with the observed data. The model’s performance was also evaluated using various statistical criteria. Results showed good correlation between IMERG and observed data, with a probability of detection (POD) of 85.42%. Overall, wavelet-based models outperformed their singular counterparts. Among the singular models, the FFNN model performed better than ELM, with satisfactory predictions till 5 days of lead time. Further, developed models have been used to forecast hourly water levels at Hayaghat gauging site of Bagmati River with different lead times from 1 hour to 10 days. For a 7-day lead time, only W-FFNN performs well, whereas none of the models performs satisfactory results for a 10-day lead time.