Integrating web-GIS and hydrological model: a case study with google maps and IHACRES in the Oak Ridges Moraine area, Southern Ontario, Canada
Bibliographic record
Abstract
The purposes of this paper are to examine the current research about the integration between GIS (Geographic Information System) and hydrological models and to present a system with the integration between Google Maps and IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) model to predict the streamflow in the Oak Ridge Moraine, Southern Ontario, Canada. Compared with other integrated systems, this system has several advantages. Firstly, this system is independent of platform and does not require the installation of the expensive GIS software. The users only need Web browser to access the whole system. Secondly, compared with the systems integrated with the web-GIS, such as ESRI ArcIMS, this system has another advantage. Because Google Maps can provide crucial spatial information including high resolution images, road network, etc., the developers only need to process part of spatial data, such as basin boundary, stream network and temporal data. This characteristic can significantly decrease the development cost and time. Finally, unlike other integrated systems, this system only needs very limited GIS and hydrology knowledge and the user interface is based on friendly Google Maps. This advantage significantly eliminates the obstacles for the public to access this integrated system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".