NTSB CAROL · Event
Event ANC24LA060
Registry · N6LU
FAA Aircraft Registry record.
Make / Model
DEHAVILLAND BEAVER DHC-2 MK.1
Year of manufacture
1954 · 70 years old at event
Engine
P&W R-985 SERIES (450 hp)
Seats / Engines
8 seats · 1 engine
Last airworthiness date
19990617
ADS-B equipped
Yes — Mode-S A7C39A
Registrant of record
MY HOLDINGS INC
Source: FAA Aircraft Registry (releasable master file).
Aircraft involved
Probable cause & findings
The pilot’s selection of an unsuitable takeoff area, resulting in the floats contacting the shallow river bottom during takeoff, and a subsequent loss of control.
Factual narrative
The pilot was departing from a shallow river in a float-equipped airplane. She reported that she was almost on step when the airplane suddenly veered to the left toward the riverbank. She reduced engine power to abort the takeoff, however the airplane impacted the riverbank and trees. The airplane sustained substantial damage to the left wing and fuselage. The pilot reported no pre-impact mechanical malfunctions or failures with the airplane that would have precluded normal operation. In the pilot’s written statement, she reported that the airplane’s floats likely encountered the shallow river bottom during the takeoff run, resulting in the sudden left veer. Source: NTSB Aviation Accident Database Retrieved: 2026-02-12
NTSB Findings
Hierarchical cause / factor breakdown from the FAA bulk avdata database. Each finding tagged C (Cause) or F (Factor).
- — Aircraft-Aircraft oper/perf/capability-Performance/control parameters-Directional control-Not attained/maintained
- — Personnel issues-Psychological-Attention/monitoring-Monitoring environment-Pilot
- — Environmental issues-Physical environment-Object/animal/substance-Tree(s)-Contributed to outcome
- — Environmental issues-Physical environment-Runway/land/takeoff/taxi surface-(general)-Contributed to outcome
Verbatim from NTSB's published report. Source file
NTSB_2024_ANC24LA060.txt.
Findings + structured fields enriched from FAA avall.mdb.
Full investigation docket on
data.ntsb.gov ↗.
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Related research
What the literature says.
Academic papers and agency reports matching this event's aircraft type or causal vocabulary (loss of control). Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- Embry-Riddle Scholarly Commons 2025 · Journal article (JAAER)
A Scoping Review of Aviation Loss of Control Inflight Research
Loss of control – inflight (LOC-I) contributes to aircraft accidents at unacceptably high rates. Significant industry efforts and research have aimed to improve LOC-I prevention, detection, and recove…
- SKYbrary (Eurocontrol) 2024 · SKYbrary article
Loss of Control In-Flight (LOC-I) — SKYbrary Knowledge Base
SKYbrary comprehensive knowledge-base entry on Loss of Control In-Flight — definitions, contributing factors, accident case studies (Air France 447, Colgan 3407), and prevention strategies.
- NTSB Aircraft Accident Reports 2022 · Accident report
Loss of Control on Takeoff in Icing Conditions — Citation 560XL
Cessna Citation 560XL fatal takeoff icing accident, March 2018. Investigation of a Citation 560XL loss-of-control takeoff accident in icing conditions.
- Semantic Scholar 2021 · Article (Aviation)
ANALYSIS OF GENERAL AVIATION FIXED-WING AIRCRAFT ACCIDENTS INVOLVING INFLIGHT LOSS OF CONTROL USING A STATE-BASED APPROACH
Inflight loss of control (LOC-I) is a significant cause of General Aviation (GA) fixed-wing aircraft accidents. The United States National Transportation Safety Board’s database provides a rich source…
- NASA NTRS 2021 · Presentation
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
- NASA NTRS 2021 · Conference Paper
Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection
We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft.
Browse the full corpus — academia portal ↗