A dynamic big data fusion and knowledge discovery approach for water resources intelligent system based on granular computing

Abstract

This study aims to achieve intelligent fusion and unified modeling to meet the requirements of multi-source and heterogeneous big data granulation for knowledge discovery in the field of water resources. The paper focuses on decision-making data granulation and knowledge discovery driven by big data in the field of water resources. It utilizes a combination of domain numerical simulation and model verification to systematically investigate decision-oriented big data multi-granularity granulation and knowledge discovery. The study reveals the mechanism and law of the transformation of management and decision-making paradigm driven by big data. This study results include the development of a granulation mechanism and a semantic fusion method for multi-source and heterogeneous big data, a multi-scale granular structure for big data, multi-granularity feature discovery and granulation method, and a multi-granularity uncertainty reasoning and knowledge discovery method. The proposed dynamic big data fusion and knowledge discovery approach effectively supports big data granulation and knowledge discovery in water resource decision-making. The study found that the proposed dynamic big data multi-granularity fusion method outperforms existing dynamic big data correlation analysis methods and greatly reduces data processing time.