{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6FG3XJMMEDQGZDFZ3NLEWZIXMD","short_pith_number":"pith:6FG3XJMM","schema_version":"1.0","canonical_sha256":"f14dbba58c20e06c8cb9db564b651760d2960d3a301806d90f29dc7f88002241","source":{"kind":"arxiv","id":"2407.15882","version":2},"attestation_state":"computed","paper":{"title":"Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.ao-ph","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arpit Kapoor, Jim Ng, Rohitash Chandra, R. Willem Vervoort, Siddharth Khedkar","submitted_at":"2024-07-20T23:45:04Z","abstract_excerpt":"In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting extreme climate events; however, large flooding events present a critical challenge due to factors such as model calibration and missing data. We present an ensemble quantile-based deep learning framework that addresses large-scale streamflow forecasts using quantile regression for uncertainty projections in prediction. We evaluate selected univariate and multivariate deep learning models and catchment strategies. F"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.15882","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-20T23:45:04Z","cross_cats_sorted":["physics.ao-ph","stat.AP","stat.ML"],"title_canon_sha256":"d11a90320de0de09a8f5cadfc161a37a64c82ab8033d10a4c70d3a84898eb1a2","abstract_canon_sha256":"84b8e3cbbf9c9ba70048681bbf75317c66fff7d4faa5c155d4450d5d2e718515"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:12:18.182190Z","signature_b64":"CUC1XozBLjoaAJGfXwwIOyf96wO1nzwwEZy0mTJRyZS2jYHcjtJINLx13YL8soZ/+PFuju5jSIcg/kFr67RHAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f14dbba58c20e06c8cb9db564b651760d2960d3a301806d90f29dc7f88002241","last_reissued_at":"2026-07-05T10:12:18.181662Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:12:18.181662Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.ao-ph","stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arpit Kapoor, Jim Ng, Rohitash Chandra, R. Willem Vervoort, Siddharth Khedkar","submitted_at":"2024-07-20T23:45:04Z","abstract_excerpt":"In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting extreme climate events; however, large flooding events present a critical challenge due to factors such as model calibration and missing data. We present an ensemble quantile-based deep learning framework that addresses large-scale streamflow forecasts using quantile regression for uncertainty projections in prediction. We evaluate selected univariate and multivariate deep learning models and catchment strategies. F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15882","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.15882/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.15882","created_at":"2026-07-05T10:12:18.181729+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.15882v2","created_at":"2026-07-05T10:12:18.181729+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15882","created_at":"2026-07-05T10:12:18.181729+00:00"},{"alias_kind":"pith_short_12","alias_value":"6FG3XJMMEDQG","created_at":"2026-07-05T10:12:18.181729+00:00"},{"alias_kind":"pith_short_16","alias_value":"6FG3XJMMEDQGZDFZ","created_at":"2026-07-05T10:12:18.181729+00:00"},{"alias_kind":"pith_short_8","alias_value":"6FG3XJMM","created_at":"2026-07-05T10:12:18.181729+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.15674","citing_title":"Quantile deep learning models for multi-step ahead time series prediction","ref_index":67,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD","json":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD.json","graph_json":"https://pith.science/api/pith-number/6FG3XJMMEDQGZDFZ3NLEWZIXMD/graph.json","events_json":"https://pith.science/api/pith-number/6FG3XJMMEDQGZDFZ3NLEWZIXMD/events.json","paper":"https://pith.science/paper/6FG3XJMM"},"agent_actions":{"view_html":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD","download_json":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD.json","view_paper":"https://pith.science/paper/6FG3XJMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.15882&json=true","fetch_graph":"https://pith.science/api/pith-number/6FG3XJMMEDQGZDFZ3NLEWZIXMD/graph.json","fetch_events":"https://pith.science/api/pith-number/6FG3XJMMEDQGZDFZ3NLEWZIXMD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD/action/storage_attestation","attest_author":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD/action/author_attestation","sign_citation":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD/action/citation_signature","submit_replication":"https://pith.science/pith/6FG3XJMMEDQGZDFZ3NLEWZIXMD/action/replication_record"}},"created_at":"2026-07-05T10:12:18.181729+00:00","updated_at":"2026-07-05T10:12:18.181729+00:00"}