The Anhui reach of the Huaihe River Basin features low-lying terrain, dense river networks, and high flood risk spatial heterogeneity, restricting regional socio-ecological sustainability. Traditional flood risk assessments suffer from incomplete indicators, subjective grading, and one-sided outputs when adopting single empirical or unsupervised clustering models. Based on disaster system theory, this study constructed a 14-indicator evaluation system covering hazard susceptibility, environmental sensitivity, and hazard-bearing vulnerability. Subjective-objective weights from Analytic Hierarchy Process (AHP) and entropy weight were fused via game theory equilibrium to build the Game Theory-AHP-Entropy Weight Coupled FCE Model (GAE-FCE Model) for comprehensive flood risk quantification. Meanwhile, principal component analysis (PCA) was utilized for indicator dimensionality reduction, and two unsupervised clustering models, namely, K-Means and Gaussian Mixture Model (GMM), were developed. Confusion matrix, Adjusted Rand Index, and correlation analysis were used to compare four model outputs. Individual models display weak correlation due to distinct risk characterization mechanisms; the ensemble scheme compensates single-model defects, achieving AUC = 0.9161, Recall = 0.9231, and F1 = 0.8889 under optimal weights, and a multi-model coupling framework was further proposed to obtain integrated flood risk zoning. Results show that five dominant factors (Distance to rivers, Topographic Roughness, NDVI, Distance to lakes, and Population Density) contribute over 59.6% of total weight. GAE-FCE reflects inundation characteristics yet lacks natural-factor comprehensiveness; clustering methods capture natural background differentiation but ignore socio-economic vulnerability, whereas ensemble coupling offsets individual model defects. High-risk zones are concentrated along major rivers, and low-risk areas mainly distribute in southern mountainous regions. This multi-model framework provides scientific support for flood prevention and territorial spatial optimization, advancing sustainable disaster risk reduction for the Huaihe River Basin and similar plain basins.

Increasing evidence indicates that microbial metabolism and mineral transformations within low-permeability clay layers, together with solute exchange between clay layers and adjacent aquifers, can jointly regulate arsenic (As) release, immobilization, and remobilization. However, how coupled C, N, S, and Fe cycling in clay layers regulates As oxidation state, solid–liquid partitioning, and transport, and how these effects extend across clay–aquifer interfaces, has not been critical narratively synthesized. This review focuses on subsurface clay layers and summarizes their hydrogeochemical properties, microbial communities, and vertical redox zonation. It examines how microbially mediated C–N–S–Fe processes influence As release, transformation, and immobilization, and further discusses the export of reactive constituents from clay layers, biogeochemical reactions at clay–aquifer interfaces, As enrichment in adjacent aquifers, and the effects of groundwater pumping on As transport. Available studies suggest that As behavior is condition-dependent and reflects interactions among microbial metabolism, electron-donor and electron-acceptor availability, mineral transformations, and interfacial transport, which together determine whether As is retained, released, or remobilized. Management implications and future research priorities are also considered. The resulting cross-scale framework links biogeochemical processes within clay layers with clay–aquifer exchange and aquifer responses, providing a basis for long-term groundwater As risk assessment, monitoring, and management.