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 appr...

Full description

Saved in:
Bibliographic Details
Published in:Geocarto international 2022-11, Vol.37 (21), p.6213-6231
Main Authors: Anand, Akash, Dinesh, Anand Singh, Srivastava, Prashant K., Chaudhary, Sumit Kumar, Varma, Atul Kumar, Kumar, Pavan
Format: Article
Language:English
Subjects:
GPM microwave imager
machine learning
Neuro-fuzzy Models
retrieval algorithm
statistical metrices
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1010-6049
1752-0762
DOI:10.1080/10106049.2021.1936208