Development of a Prototype Web-Based Decision Support System for Watershed Management
Bibliographic record
Abstract
Using distributed hydrological models to evaluate the effectiveness of reducing non-point source pollution by applying best management practices (BMPs) is an important support to decision making for watershed management. However, complex interfaces and time-consuming simulations of the models have largely hindered the applications of these models. We designed and developed a prototype web-based decision support system for watershed management (DSS-WMRJ), which is user friendly and supports quasi-real-time decision making. DSS-WMRJ is based on integrating an open-source Web-based Geographical Information Systems (Web GIS) tool (Geoserver), a modeling component (SWAT, Soil and Water Assessment Tool), a cloud computing platform (Hadoop) and other open source components and libraries. In addition, a private cloud is used in an innovative manner to parallelize model simulations, which are time consuming and computationally costly. Then, the prototype DSS-WMRJ was tested with a case study. Successful implementation and testing of the prototype DSS-WMRJ lay a good foundation to develop DSS-WMRJ into a fully-fledged tool for watershed management. DSS-WMRJ can be easily customized for use in other watersheds and is valuable for constructing other environmental decision support systems, because of its performance, flexibility, scalability and economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".