Web-based hydrological modeling system for flood forecasting and risk mapping
Bibliographic record
Abstract
Mechanism of flood forecasting is a complex system, which involves precipitation, drainage characterizes, land use/cover types, ground water and runoff discharge. The application of flood forecasting model require the efficient management of large spatial and temporal datasets, which involves data acquisition, storage, pre-processing and manipulation, analysis and display of model results. The extensive datasets usually involve multiple organizations, but no single organization can collect and maintain all the multidisciplinary data. The possible usage of the available datasets remains limited primarily because of the difficulty associated with combining data from diverse and distributed data sources. Difficulty in linking data, analysis tools and model is one of the barriers to be overcome in developing real-time flood forecasting and risk prediction system. The current revolution in technology and online availability of spatial data, particularly, with the construction of Canadian Geospatial Data Infrastructure (CGDI), a lot of spatial data and information can be accessed in real-time from distributed sources over the Internet to facilitate Canadians' need for information sharing in support of decision-making. This has resulted in research studies demonstrating the suitability of the web as a medium for implementation of flood forecasting and flood risk prediction. Web-based hydrological modeling system can provide the framework within which spatially distributed real-time data accessed remotely to prepare model input files, model calculation and evaluate model results for flood forecasting and flood risk prediction. This paper will develop a prototype web-base hydrological modeling system for on-line flood forecasting and risk mapping in the Oak Ridges Moraine (ORM) area, southern Ontario, Canada, integrating information retrieval, analysis and model analysis for near real time river runoff prediction, flood frequency prediction, flood risk and flood inundation area prediction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".