{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SKM2QFIZHS6OTW2Q7ZBVXB36D4","short_pith_number":"pith:SKM2QFIZ","schema_version":"1.0","canonical_sha256":"9299a815193cbce9db50fe435b877e1f25857c70bca41f390dcbbc71466c7842","source":{"kind":"arxiv","id":"1901.09583","version":1},"attestation_state":"computed","paper":{"title":"ML for Flood Forecasting at Scale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ajai Tirumali, Ami Wiesel, Avinatan Hassidim, Gal Elidan, Guy Shalev, Mor Schlesinger, Oleg Zlydenko, Pete Giencke, Ran El-Yaniv, Sella Nevo, Vova Anisimov, Yossi Matias, Yotam Gigi, Zach Moshe","submitted_at":"2019-01-28T10:23:29Z","abstract_excerpt":"Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate. Machine learning (ML) is primed to be useful in this scenario: learned models often surpass human experts in complex high-dimensional scenarios, and the framework of transfer or multitask learning is an appealing solution for leveraging local signals to achieve improved global per"},"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":"1901.09583","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-28T10:23:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"661d5bc1f9f4bcea0be5c6b11940df8c607bad32ec3adb56c9a7942bac14cdae","abstract_canon_sha256":"d3ea3e48631b67c290e4be5f4ce053ae1ff02c711f01a5b80bb1afb51b79db7a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:24.392208Z","signature_b64":"ERMnBV0yW28U7KljLqQIA4vKF0bxWLOnpwcfZeBD55DoNhS+g+25uAHV1BAPBX6wGbX5cRCkYpYQmG9pZ3E8Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9299a815193cbce9db50fe435b877e1f25857c70bca41f390dcbbc71466c7842","last_reissued_at":"2026-05-17T23:55:24.391852Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:24.391852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML for Flood Forecasting at Scale","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ajai Tirumali, Ami Wiesel, Avinatan Hassidim, Gal Elidan, Guy Shalev, Mor Schlesinger, Oleg Zlydenko, Pete Giencke, Ran El-Yaniv, Sella Nevo, Vova Anisimov, Yossi Matias, Yotam Gigi, Zach Moshe","submitted_at":"2019-01-28T10:23:29Z","abstract_excerpt":"Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate. Machine learning (ML) is primed to be useful in this scenario: learned models often surpass human experts in complex high-dimensional scenarios, and the framework of transfer or multitask learning is an appealing solution for leveraging local signals to achieve improved global per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09583","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1901.09583","created_at":"2026-05-17T23:55:24.391909+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09583v1","created_at":"2026-05-17T23:55:24.391909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09583","created_at":"2026-05-17T23:55:24.391909+00:00"},{"alias_kind":"pith_short_12","alias_value":"SKM2QFIZHS6O","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"SKM2QFIZHS6OTW2Q","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"SKM2QFIZ","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4","json":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4.json","graph_json":"https://pith.science/api/pith-number/SKM2QFIZHS6OTW2Q7ZBVXB36D4/graph.json","events_json":"https://pith.science/api/pith-number/SKM2QFIZHS6OTW2Q7ZBVXB36D4/events.json","paper":"https://pith.science/paper/SKM2QFIZ"},"agent_actions":{"view_html":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4","download_json":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4.json","view_paper":"https://pith.science/paper/SKM2QFIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09583&json=true","fetch_graph":"https://pith.science/api/pith-number/SKM2QFIZHS6OTW2Q7ZBVXB36D4/graph.json","fetch_events":"https://pith.science/api/pith-number/SKM2QFIZHS6OTW2Q7ZBVXB36D4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4/action/storage_attestation","attest_author":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4/action/author_attestation","sign_citation":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4/action/citation_signature","submit_replication":"https://pith.science/pith/SKM2QFIZHS6OTW2Q7ZBVXB36D4/action/replication_record"}},"created_at":"2026-05-17T23:55:24.391909+00:00","updated_at":"2026-05-17T23:55:24.391909+00:00"}