DESIGNING AN INTEGRATED SOLUTION FOR THE ALA WAI WATERSHED: ANALYSIS OF PROPOSALS, GLOBAL FLOOD MITIGATION PROJECTS, AND WATER QUALITY IMPROVEMENT TECHNOLOGY
Bibliographic record
Abstract
The Ala Wai Watershed in Honolulu, Hawaii, is situated in a densely populated region that faces unique challenges, including severe flood risks, steep topography, intense storm events, and an outdated single outlet canal system, constructed in the 1920s.The primary objective of this research was to develop an integrated solution that addresses flood mitigation, environmental restoration, and repairs to the existing, aging Ala Wai Canal.This research examines the existing problem, including a comprehensive evaluation of the major factors at play, such as flood risks, environmental concerns, outdated infrastructure, and cultural impacts.It also analyzes various existing proposals for flood mitigation within the Ala Wai Watershed with an understanding of all the major factors.The U.S. Army Corps of Engineers' (USACE) Tentatively Selected Plan (TSP) for the Ala Wai Watershed Flood Mitigation Project is explored in detail, including the economic viability of the proposal.In addition, global flood mitigation projects such as the Delta Works in the Netherlands and the Glasgow Smart Canal in Scotland are reviewed to provide valuable insights into potential solutions for the Ala Wai Watershed.Furthermore, canal flood mitigation efforts in New Orleans and New Jersey are highlighted, offering insights into projects that demonstrated effective coordination among United States federal agencies, such as the USACE and the Federal Emergency Management Agency (FEMA), as well as other key stakeholders.Going beyond flood mitigation, environmental technologies utilized both locally and internationally were analyzed for their cost, effectiveness, and overall applicability for the Ala Wai Canal.Approaches varied widely, from surface-level systems aimed at removing trash and debris to advanced technologies designed to improve water quality using microorganisms.Following an in-depth review of the problem, proposed solutions, global flood relief projects, and water quality improvement initiatives, the author presents an integrated solution for the Ala Wai Watershed.This proposed solution addresses flood mitigation while also tackling broader environmental and infrastructural challenges, providing a long-term solution that utilizes modern engineering practices to retrofit the existing system.The proposed solution is an integrated strategy that incorporates best-fit systems and practices identified throughout this research.For flood control, the author proposes constructing floodwalls on the mauka side of the Ala Wai Canal to match the height of the existing makai walls, addressing flood risk Integrated Proposal for the Ala Wai Watershed iii David Beebe | UNIVERSITY OF HAWAIIdisparities.Additionally, the proposal includes constructing a second canal outlet under Kapahulu Avenue to enhance water capacity and provide an alternative flow path, which will also facilitate incremental renovations of the existing canal infrastructure.Environmental improvements in the Author's Integrated Proposal focus on enhancing water quality utilizing Genki Balls, invasive species removal and fencing, and the implementation of the Spinning Cogs system for debris management.The Author's Integrated Proposal includes plans to upgrade the Ala Wai Canal's structure with concrete flooring and walls.While the proposal's estimated cost of $991 million is substantial, it offers significant benefits, including improved flood protection, enhanced water quality, and modernized infrastructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".