Auditable Knowledge-Graph Screening of Landslides for River Blockage and Dammed-Lake Assessment

Abstract

Remote-sensing landslide inventories identify mapped slope failures, but follow-up assessment must decide which objects warrant earlier inspection for river blockage, landslide-dammed lake potential, or downstream disaster-chain effects. We address this post-inventory prioritization problem in the Ludian-Zhaotong-Niulanjiang alpine-gorge region of Yunnan, Southwest China. A mechanism-oriented knowledge graph (KG) links mapped landslides with material supply, terrain setting, local waterway proximity, standardized river-network context, provenance, and post-scoring contextual evidence. Predefined screening rules reduce 10,842 inventory polygons to 193 reference assessment units. The graph contains 11,590 nodes and 170,800 typed relations, and the retained units are ordered using the Mechanism-Balanced Evidence Index (MBEI), a non-optimized equal-weight score that expresses relative inspection priority rather than blockage probability. Contextual-label diagnostics show that hydrologic setting accounts for much of the observed ordering. For MBEI, the normalized discounted cumulative gain at rank 20 (NDCG@20) is 0.852 and is interpreted as a contextual-ordering diagnostic rather than a validated performance metric. A four-domain score that reduces repeated weighting of correlated variables has a Spearman rank correlation of 0.966 with MBEI and retains 16 of its Top-20 units, while 1st–99th percentile-clipped normalization gives a correlation of 0.999 and retains all 20. The numerical ranking can be reproduced from an exported table; the added role of the KG is to keep eligibility rules, score components, source datasets, evidence status, audit paths, and interpretation limits connected to each unit. The resulting shortlist is intended to guide high-resolution image interpretation, field inspection, and hydraulic assessment, not to provide polygon-level event confirmation.