Abstract

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 location (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 endorse these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms.

摘要

对于许多水文气象应用来说，使用基于卫星的测量进行有效的降雨率估计是必不可少的。随着卫星产品和检索算法的最新进展，降雨率估计不断改进。本研究提供了三种混合机器学习算法的比较性能评估，即自适应神经模糊推理系统 (ANFIS)、动态进化神经模糊推理系统 (DENFIS) 和混合模糊推理系统 (HYFIS)，用于使用全球降水估计降雨率测量 (GPM) 的微波成像仪 (GMI) 和基于地面的 Disdrometer 数据。在印度地区的四个不同位置（陆地和海洋）进行了原位采样，并使用了不同的统计指标来评估这些模型的性能。结果表明，HYFIS算法比ANFIS和DENFIS算法提供了更好的降雨率估计。该研究认可这些用于生成准确降水产品的神经模糊模型，可被视为未来卫星检索算法的替代方案。