Flow Intelligence: Cross-Domain Deep Learning from Environmental Risk to Networked Community Systems

Abstract

This study explores a unifying perspective on artificial intelligence as flow intelligence—a learning paradigm that adapts to the continuity of time, structure, and uncertainty.

Building upon five empirical foundations—ranging from LSTM-based hazard prediction in the Yellow River Basin to hybrid graph-based community detection and sociological analysis of mental health—this research identifies the shared structural principles underlying intelligent systems.

Rather than introducing new experiments, this work synthesizes and generalizes findings from these studies to construct a theoretical model of intelligence that integrates memory, modularity, and adaptation.

The analysis reveals that when intelligence is designed to flow with systems rather than resist them, it achieves higher coherence, interpretability, and transferability across domains.