{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/0bd17940-5711-48f1-963c-92e122ef7d69","identifier":"0bd17940-5711-48f1-963c-92e122ef7d69","url":"https://froggit.ai/public/capsules/0bd17940-5711-48f1-963c-92e122ef7d69","name":"FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale","text":"# FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale\n\nSource-backed public reference for arXiv:2604.16265.\n\n**Authors:** Aswathi Mundayatt, Jaya Sreevalsan-Nair\n**Primary source:** https://arxiv.org/abs/2604.16265\n**Published:** 2026-04-17T17:23:55Z\n**Updated:** 2026-04-17T17:23:55Z\n**Categories:** cs.LG\n\n## Abstract Summary\nExisting multi-hazard susceptibility mapping (MHSM) studies often rely on spatially uniform models, treat hazards independently, and provide limited representation of cross-hazard dependence and uncertainty. To address these limitations, this study proposes a deep learning (DL) workflow for joint flood-landslide multi-hazard susceptibility mapping (FL-MHSM) that combines two-level spatial partitioning, probabilistic Early Fusion (EF), a tree-based Late Fusion (LF) baseline, and a soft-gating Mixture of Experts (MoE) model, with MoE serving as final predictive model. The proposed design preserves spatial heterogeneity through zonal partitions and enables data-parallel large-area prediction using overlapping lattice grids. In Kerala, EF remained competitive with LF, improving flood recall from 0.816 to 0.840 and reducing Brier score from 0.092 to 0.086, while MoE provided strongest performance for flood susceptibility, achieving an AUC-ROC of 0.905, recall of 0.930, and F1-score of 0.722. In Nepal, EF similarly improved flood recall from 0.820 to 0.858 and reduced Brier score from 0.057 to 0.049 relative to LF, while MoE outperformed both EF and LF for landslide susceptibility, achieving an AUC-ROC of 0.914, recall of 0.901, and F1-score of 0.559. GeoDetector analysis of MoE...\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deploym","keywords":["cs.LG"],"about":[],"citation":["https://arxiv.org/abs/2604.16265"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-04-20T06:00:03.217000Z","dateModified":"2026-06-19T03:22:45Z","isBasedOn":"https://arxiv.org/abs/2604.16265","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"a3cbef1d02793727dc3fccacbb080a17636b2cdfd1a00481fb97c481c8de322e"}]}