Streamflow data analysis using persistent homology

Abstract

Understanding streamflow data can be important climatic indicators for environmental risk problems such as flooding. Recently, topological data analysis (TDA) gave a new insight in data analysis. The main idea in TDA is to used results based on topology to develop tools for studying qualitative features or shape-like structure of data. Persistent homology (PH) is one of the tools in TDA that focuses on aspects of topological features in data that persists across multiple scales. So the question here is, can PH detect flood based on streamflow data. Therefore, the first attempt of streamflow analysis using PH was conducted at Guillemard Bridge Station, Kelantan River, Malaysia. Analysis for streamflow data during dry period, wet period and flood events were perform using TDA approach. The analysis result shows that PH can detect the pattern of topological features in streamflow data. The analysis suggests that the presence of short-lived topological features indicates dry period while long-lived topological features for wet period. Based on the streamflow data of flood events, PH consistently captured long-lived topological features of the data.