Rainfall rate estimation over India using global precipitation measurement's microwave imager datasets and different variants of fuzzy information system

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)'s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms.