CLOUD-BASED FLUORIDE POLLUTION DATA MINING AND 3D RISK VISUALIZATION FOR ISOETES SINENSIS IN THE YANGTZE RIVER, CHINA

This study investigates fluoride pollution in the Yangtze River Basin, China, focusing on its sources and ecological impacts on the endangered aquatic plant Isoetes sinensis.Utilizing a cloud computing-based framework, the research integrates data mining techniques-Random Forest (RF) and Principal Component Analysis (PCA)-with Geographic Information System (GIS)-based 3D visualization to enhance environmental monitoring and risk assessment.Fluoride data from 2010 to 2024 reveal an increase of concentrations from 0.88 mg/L to 1.34 mg/L, with industrial hotspots near Hefei and Nanjing surpassing the national limit of 1.0 mg/L.Source attribution indicates industrial wastewater as the dominant contributor (74%), followed by geochemical processes (16%) and agricultural runoff (10%).Species Sensitivity Distribution (SSD) modeling identifies a hazardous concentration threshold (HC) of 0.5 mg/L, with Risk Quotients (RQ) exceeding 2.0 in high-risk zones.The 3D GIS visualization clearly maps pollution intensity and vulnerable habitats, supporting targeted conservation and mitigation measures, such as stricter industrial discharge regulation, habitat restoration, and ecological buffer zone implementation.Results highlight the urgent need for stricter industrial discharge regulations and focused protection strategies for Isoetes sinensis.This integrative approach offers a scalable solution for river basin management and biodiversity conservation.