Automated Lake Wide Erosion Predictions and Economic Damages on Lake Ontario
Bibliographic record
Abstract
The shoreline of Lake Ontario has been exposed to erosion and sedimentation processes since the retreat of the Wisconsin Glaciation, approximately 12,000 YBP. In modern times, these processes continue to shape the shoreline along with human influences, such as shoreline protection, harbor construction, dredging and beach nourishment. Water levels play a key role in how we manage coastal hazards along developed shorelines. For example, during periods of high lake levels shoreline erosion rates accelerate beyond the long term background rate and riparian land owners are often faced with the economic burden or building shoreline protection to save their house. During low lake levels, navigation is often a problem that requires intervention in the form of channel dredging. The International Joint Commission (IJC) is presently re-evaluating the operational procedures for the Moses-Saunders Power Dam in Massena, New York, which controls the water levels of Lake Ontario and the flows of the St. Lawrence River. The weekly discharge rates at the dam range from 5,000 to 10,000 cubic meters per second and are regulated by a series of rules developed under the Boundary Waters Treaty of 1909 between the United States and Canada. The current regulation plan is 1958D. New plans presently under consideration require complete impact evaluations for the system stakeholders that are sensitive to water level fluctuations, such as riparian property owners, the natural environment, recreational boating, commercial navigation and hydro electric power generation. Baird & Associates were retained by the Buffalo District USACE to evaluate the impacts of water levels generate by new plans on erosion and flooding hazards for riparian property. Refer to Zuzek and Nairn (these proceedings) for a discussion of the flooding methodology. The study area included over 4,000 km of river and lake shoreline and 21,000 riparian properties. Baird has been involved in studies of shoreline erosion on the Great Lakes for over 20 years and this knowledge was recently summarized in a Chapter 5 Part III of the Coastal Engineering Manual prepared by the USACE. In addition, the development of the COSMOS model (Nairn and Southgate, 1993), which is capable of modeling erosion processes for the wide range of shore types (i.e. geology) found along the shores of the Great Lakes, provided the necessary fine scale modeling tool. Development of the Flood and Erosion Prediction System (FEPS) began during the Lake Michigan Potential Damages in the late 1990s.. The FEPS links GIS technology, engineering models such as COSMOS, and custom software applications. At a local scale, there was sufficient knowledge, experience and capabilities to predict shoreline response for alternative regulation plans developed by the IJC. However, the principal challenge was the application of this skill to over 4,000 km of shoreline at a very fine resolution (i.e. individual property parcels). In addition, the individuals who are developing new regulation plans required an automated procedure where a 101 year water level hydrograph is entered in the model, the physics of erosion is predicted, and economic damages are exported. To accomplish these goals addition functionality was required in the FEPS, such as a Relational Database Module to store information for the economic analysis. The key components of the investigation, along with samples of the predictive capabilities of the FEPS, will be summarized.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".