{ "id": "AIBD-0098", "title": "Philippine Red Cross activated its typhoon Early Action Protocol on 510's machine-learning impact forecast before Storm Nalgae (October 2022)", "summary": "510 ML impact forecast hit the trigger on 27 October 2022; PRC installed shelter kits for 78 Aurora households before Storm Nalgae", "date_observed": "2022-10-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent.", "category": "protective response", "unexpected": false, "purpose": "Forecast typhoon housing damage per municipality to trigger pre-agreed early actions before landfall.", "system": "510 impact-based forecasting (IBF) model for typhoons in the Philippines, operated through the 510 dashboard with PAGASA forecasts", "model_version": "eXtreme Gradient Boosting (XGBoost)", "developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross", "deployer": "Philippine Red Cross, with IFRC anticipatory DREF funding", "location": "Dipaculao, Aurora Province, Philippines", "domain": "Disaster risk reduction and forecast-based financing", "segment": "Red Cross National Society", "benefit_type": "Reduced Risk", "taxonomy_note": "Shelter reinforcement before the storm reduces potential harm, but the post-storm survey gives no counterfactual.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ], "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." }, "evidence": [ { "kind": "report", "citation": "IFRC and Philippine Red Cross (2022). Early Action Protocol Activation Notification: Philippines - Typhoons (EAP2019PH01, MDRPH049).", "title": "EAP Activation Notification, Philippines Typhoons (EAP2019PH01)", "url": "https://adore.ifrc.org/Download.aspx?FileId=590819", "date": "2022-10-27", "written_by": "deployer", "quote": "shelter damages are projected up to 10 per cent in different municipalities in the priority areas under the Early Action Protocol (EAP) for typhoon" }, { "kind": "report", "citation": "IFRC and Philippine Red Cross (2023). Early Action Protocol Activation Final Report: Philippines, Typhoon Paeng (MDRPH049). Dated 30 June 2023; PDF file timestamp 18 January 2024.", "title": "Early Action Protocol Activation Final Report, Philippines Typhoon Paeng", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr", "date": "2023-06-30", "written_by": "deployer", "quote": "Despite of the challenges, 100 percent of 78 households who received the kits were successfully installed/reinforced." }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309–329.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "In 2019, the 510 model was approved as the trigger model for the early action protocol for typhoons" }, { "kind": "other", "citation": "Free, R., van den Homberg, M., Sperna Weiland, F., Teklesadik, A., Melillo, M., Bogaard, T. (2022). Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events. EGU General Assembly 2022, EGU22-10474.", "title": "Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events", "url": "https://meetingorganizer.copernicus.org/EGU22/EGU22-10474.html", "date": "2022-05-23", "written_by": "makers", "quote": "An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons" } ], "replication": { "produced_by": [ "510 IBF damage model fed by PAGASA typhoon forecasts", "Pre-approved EAP with a 10% projected-damage trigger", "IFRC anticipatory DREF funds released on trigger", "Pre-positioned shelter kits, trained carpenters and pre-identified households" ], "must_hold": [ "Lead time near 72 hours", "Forecast track and intensity accuracy", "Weather permitting installation before landfall", "Local government authorization in no-build zones" ], "echoes": [ { "date": "2019-11-30", "setting": "Typhoon Kammuri 2019: EAP newly approved; IFRC DREF noted the forecast-based trigger might not yet be reached", "model_version": null, "outcome": "unknown", "source": "IFRC DREF MDRPH037, https://adore.ifrc.org/Download.aspx?FileId=271567" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "536", "title": "NJ Transit's Use of Modeling Software Miscalculated Storm Surge Threat Level", "url": "https://incidentdatabase.ai/cite/536", "severity": null, "group": "organizational", "basis": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the same way." }, "headline": "510's ML forecast triggers PRC shelter kits before Storm Nalgae", "metric": "installed shelter kits for 78 Aurora households before Storm Nalgae", "comparator": null, "later": { "status": "persisted", "date": "2023-06-30", "note": "The IFRC final report of 30 June 2023 records all 78 households served and houses reinforced before impact, but a PRC lessons-learned workshop judged the trigger high and recommended reviewing the typhoon EAP.", "quote": "In addition, the trigger was considered high according to the solicited lessons from Paeng Lesson learned Workshop conducted by PRC with the support IFRC. Thus, one of the recommendations gathered is to review and revisit the Typhoon EAP.", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr" }, "value": null, "developer_country": "Netherlands", "deployer_country": "Philippines", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 536 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/536", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the final report's date to its cover date of 30 June 2023 (18 January 2024 was the PDF file timestamp) and the notification's date to its launch date of 27 October 2022, marked the NHESS paper and EGU abstract independent because 510 staff are co-authors rather than lead authors, and restated the EAP trigger as at least 10% of houses totally damaged in more than three municipalities 72 hours before landfall, with the lead-time condition waived at 56 hours.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://adore.ifrc.org/Download.aspx?FileId=590819; https://adore.ifrc.org/Download.aspx?FileId=271567", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://nhess.copernicus.org/articles/24/309/2024/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://en.wikipedia.org/wiki/Philippine_Red_Cross", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, the makers' other and two reports from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0098", "catalog_version": "1.0.0", "source_fingerprint": "4d27685232098c66182082682b2e51601106d680d084eeb7882325e3cdc5c1ac", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0098-source-0001", "AIBD-0098-source-0002", "AIBD-0098-source-0003", "AIBD-0098-source-0004" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross", "deployer": "Philippine Red Cross, with IFRC anticipatory DREF funding" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "The 510 model forecast that damage to housing would exceed the threshold for early action in Aurora. Its forecasts incorporated data from PAGASA.", "editor_note": "" } }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The ML impact forecast was the trigger. Under the EAP, early actions fire on the forecast wind impact on housing 72 hours before landfall. The trigger: at least 10% of houses totally damaged in more than three municipalities. The notification records the Aurora municipalities as “Met (More than 10%)”. The 72-hour lead-time condition was not met. The final report says the 56-hour lead time was exceptionally granted. The notification says the projection came from the 510/IBF forecast incorporating PAGASA data. PRC staff confirmed chapter capacity and chose municipalities. The outcome claim is PRC's own post-storm survey. The NHESS paper and EGU abstract on the model have 510 staff as co-authors, not lead authors." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The deployer's account", "basis": "Sources: the makers' peer-reviewed study, the makers' other and two reports from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the same way.", "links": [ { "database": "AI Incident Database", "identifier": "536", "title": "NJ Transit's Use of Modeling Software Miscalculated Storm Surge Threat Level", "url": "https://incidentdatabase.ai/cite/536", "group": "organizational", "basis": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the same way.", "relation": "comparable_harm" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Existing paired_incident classification; no new source review.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "installed shelter kits for 78 Aurora households before Storm Nalgae", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "The planned 72-hour lead time was not met; the final report records an exceptional approval with 56 hours remaining. The Philippine Red Cross’s own survey supplies the reported benefits after the storm.", "editor_note": "" } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "536", "title": "NJ Transit's Use of Modeling Software Miscalculated Storm Surge Threat Level", "url": "https://incidentdatabase.ai/cite/536", "group": "organizational", "basis": "Storm-impact model used for pre-landfall operational decisions underestimated Sandy's surge and NJ Transit lost equipment; 510's typhoon impact model triggers Red Cross action the same way." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "persisted", "date": "2023-06-30", "note": "The IFRC final report of 30 June 2023 records all 78 households served and houses reinforced before impact, but a PRC lessons-learned workshop judged the trigger high and recommended reviewing the typhoon EAP.", "quote": "In addition, the trigger was considered high according to the solicited lessons from Paeng Lesson learned Workshop conducted by PRC with the support IFRC. Thus, one of the recommendations gathered is to review and revisit the Typhoon EAP.", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "value_present", "value": { "kind": "text", "text": "Philippine Red Cross staff checked local capacity and selected the municipalities for action." }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "ai_role", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0098" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "Shelter reinforcement before the storm reduces potential harm, but the post-storm survey gives no counterfactual." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "78 households had roofs and walls reinforced before landfall. PRC reports they mitigated wind impact (implementer survey, no counterfactual)." }, { "group": "organizational", "depth": 1, "reason": "First completed activation of the Philippine typhoon EAP. Lessons on lead time and weather constraints documented in the final report." }, { "group": "societal", "depth": 1, "reason": "Small scale: 390 people reached in one municipality." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Forecast typhoon housing damage per municipality to trigger pre-agreed early actions before landfall." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Dipaculao, Aurora Province, Philippines", "developer_country": "Netherlands", "deployer_country": "Philippines" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Forecast typhoon housing damage per municipality to trigger pre-agreed early actions before landfall." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2022-10-27", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 536 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/536", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the final report's date to its cover date of 30 June 2023 (18 January 2024 was the PDF file timestamp) and the notification's date to its launch date of 27 October 2022, marked the NHESS paper and EGU abstract independent because 510 staff are co-authors rather than lead authors, and restated the EAP trigger as at least 10% of houses totally damaged in more than three municipalities 72 hours before landfall, with the lead-time condition waived at 56 hours.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://adore.ifrc.org/Download.aspx?FileId=590819; https://adore.ifrc.org/Download.aspx?FileId=271567", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://nhess.copernicus.org/articles/24/309/2024/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://en.wikipedia.org/wiki/Philippine_Red_Cross", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "510 IBF damage model fed by PAGASA typhoon forecasts", "Pre-approved EAP with a 10% projected-damage trigger", "IFRC anticipatory DREF funds released on trigger", "Pre-positioned shelter kits, trained carpenters and pre-identified households" ], "must_hold": [ "Lead time near 72 hours", "Forecast track and intensity accuracy", "Weather permitting installation before landfall", "Local government authorization in no-build zones" ], "echoes": [ { "date": "2019-11-30", "setting": "Typhoon Kammuri 2019: EAP newly approved; IFRC DREF noted the forecast-based trigger might not yet be reached", "outcome": "unknown", "source": "IFRC DREF MDRPH037, https://adore.ifrc.org/Download.aspx?FileId=271567" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "510 IBF damage model fed by PAGASA typhoon forecasts", "Pre-approved EAP with a 10% projected-damage trigger", "IFRC anticipatory DREF funds released on trigger", "Pre-positioned shelter kits, trained carpenters and pre-identified households" ], "must_hold": [ "Lead time near 72 hours", "Forecast track and intensity accuracy", "Weather permitting installation before landfall", "Local government authorization in no-build zones" ], "echoes": [ { "date": "2019-11-30", "setting": "Typhoon Kammuri 2019: EAP newly approved; IFRC DREF noted the forecast-based trigger might not yet be reached", "outcome": "unknown", "source": "IFRC DREF MDRPH037, https://adore.ifrc.org/Download.aspx?FileId=271567" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 536 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/536", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the final report's date to its cover date of 30 June 2023 (18 January 2024 was the PDF file timestamp) and the notification's date to its launch date of 27 October 2022, marked the NHESS paper and EGU abstract independent because 510 staff are co-authors rather than lead authors, and restated the EAP trigger as at least 10% of houses totally damaged in more than three municipalities 72 hours before landfall, with the lead-time condition waived at 56 hours.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://adore.ifrc.org/Download.aspx?FileId=590819; https://adore.ifrc.org/Download.aspx?FileId=271567", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what happened since, the model version from the cited sources.", "source": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr; https://nhess.copernicus.org/articles/24/309/2024/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://www.rodekruis.nl/contact/; https://en.wikipedia.org/wiki/Philippine_Red_Cross", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Disaster risk reduction and forecast-based financing", "segment": "Red Cross National Society" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "IFRC and Philippine Red Cross (2022). Early Action Protocol Activation Notification: Philippines - Typhoons (EAP2019PH01, MDRPH049).", "title": "EAP Activation Notification, Philippines Typhoons (EAP2019PH01)", "url": "https://adore.ifrc.org/Download.aspx?FileId=590819", "date": "2022-10-27", "written_by": "deployer", "quote": "shelter damages are projected up to 10 per cent in different municipalities in the priority areas under the Early Action Protocol (EAP) for typhoon" }, { "kind": "report", "citation": "IFRC and Philippine Red Cross (2023). Early Action Protocol Activation Final Report: Philippines, Typhoon Paeng (MDRPH049). Dated 30 June 2023; PDF file timestamp 18 January 2024.", "title": "Early Action Protocol Activation Final Report, Philippines Typhoon Paeng", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr", "date": "2023-06-30", "written_by": "deployer", "quote": "Despite of the challenges, 100 percent of 78 households who received the kits were successfully installed/reinforced." }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309–329.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "In 2019, the 510 model was approved as the trigger model for the early action protocol for typhoons" }, { "kind": "other", "citation": "Free, R., van den Homberg, M., Sperna Weiland, F., Teklesadik, A., Melillo, M., Bogaard, T. (2022). Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events. EGU General Assembly 2022, EGU22-10474.", "title": "Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events", "url": "https://meetingorganizer.copernicus.org/EGU22/EGU22-10474.html", "date": "2022-05-23", "written_by": "makers", "quote": "An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0098-source-0001", "AIBD-0098-source-0002", "AIBD-0098-source-0003", "AIBD-0098-source-0004" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "report", "citation": "IFRC and Philippine Red Cross (2022). Early Action Protocol Activation Notification: Philippines - Typhoons (EAP2019PH01, MDRPH049).", "title": "EAP Activation Notification, Philippines Typhoons (EAP2019PH01)", "url": "https://adore.ifrc.org/Download.aspx?FileId=590819", "date": "2022-10-27", "written_by": "deployer", "quote": "shelter damages are projected up to 10 per cent in different municipalities in the priority areas under the Early Action Protocol (EAP) for typhoon" }, { "kind": "report", "citation": "IFRC and Philippine Red Cross (2023). Early Action Protocol Activation Final Report: Philippines, Typhoon Paeng (MDRPH049). Dated 30 June 2023; PDF file timestamp 18 January 2024.", "title": "Early Action Protocol Activation Final Report, Philippines Typhoon Paeng", "url": "https://go-api.ifrc.org/api/DownloadFile/67581/MDRPH049eapfr", "date": "2023-06-30", "written_by": "deployer", "quote": "Despite of the challenges, 100 percent of 78 households who received the kits were successfully installed/reinforced." }, { "kind": "peer-reviewed study", "citation": "Kooshki Forooshani, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., et al. (2024). Towards a global impact-based forecasting model for tropical cyclones. Natural Hazards and Earth System Sciences 24, 309–329.", "title": "Towards a global impact-based forecasting model for tropical cyclones", "url": "https://nhess.copernicus.org/articles/24/309/2024/", "date": "2024-02-01", "written_by": "makers", "quote": "In 2019, the 510 model was approved as the trigger model for the early action protocol for typhoons" }, { "kind": "other", "citation": "Free, R., van den Homberg, M., Sperna Weiland, F., Teklesadik, A., Melillo, M., Bogaard, T. (2022). Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events. EGU General Assembly 2022, EGU22-10474.", "title": "Extending a ML impact-based forecasting model for typhoons in the Philippines with a rainfall threshold for consecutive landslide events", "url": "https://meetingorganizer.copernicus.org/EGU22/EGU22-10474.html", "date": "2022-05-23", "written_by": "makers", "quote": "An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "510 ML impact forecast hit the trigger on 27 October 2022; PRC installed shelter kits for 78 Aurora households before Storm Nalgae", "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The model forecast damage to housing before a typhoon." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "XGBoost." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "eXtreme Gradient Boosting (XGBoost)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "510 impact-based forecasting (IBF) model for typhoons in the Philippines, operated through the 510 dashboard with PAGASA forecasts" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "510, an initiative of the Netherlands Red Cross, with the Philippine Red Cross and German Red Cross" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Philippine Red Cross activated its typhoon Early Action Protocol on 510's machine-learning impact forecast before Storm Nalgae (October 2022)", "headline": "510's ML forecast triggers PRC shelter kits before Storm Nalgae" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "4d27685232098c66182082682b2e51601106d680d084eeb7882325e3cdc5c1ac" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Philippine Red Cross (PRC) approved its Early Action Protocol (EAP) for typhoons in December 2019. Its impact-based forecasting (IBF) model, developed with 510 (Netherlands Red Cross), applies machine learning (ML) to predict housing damage per municipality from typhoon forecasts. As Tropical Storm Paeng (Nalgae) approached on 27 October 2022, the 510/IBF forecast projected up to 10 percent shelter damage in priority municipalities. That hit the trigger at 56 hours' lead time. With IFRC anticipatory DREF funds, PRC activated it in Aurora Province. On 27 to 29 October, 40 workers distributed and installed shelter strengthening kits for 78 of 100 planned households in Dipaculao before landfall. PRC's post-storm survey reports all 78 kits installed, 390 people reached. CHF 7,002 of the CHF 117,555 budget was spent.", "replication": { "produced_by": [ "510 IBF damage model fed by PAGASA typhoon forecasts", "Pre-approved EAP with a 10% projected-damage trigger", "IFRC anticipatory DREF funds released on trigger", "Pre-positioned shelter kits, trained carpenters and pre-identified households" ], "must_hold": [ "Lead time near 72 hours", "Forecast track and intensity accuracy", "Weather permitting installation before landfall", "Local government authorization in no-build zones" ], "echoes": [ { "date": "2019-11-30", "setting": "Typhoon Kammuri 2019: EAP newly approved; IFRC DREF noted the forecast-based trigger might not yet be reached", "outcome": "unknown", "source": "IFRC DREF MDRPH037, https://adore.ifrc.org/Download.aspx?FileId=271567" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 35, "reviewed": 0, "record_mapped": 35, "not_assessed": 56, "complete": false, "by_state": { "value_present": 35, "not_reported": 0, "unresolved": 0, "not_assessed": 56, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }