Integrating Sponge City Requirements into the Management of Urban Development Land: An Improved Methodology for Sponge City Implementation
Bibliographic record
Abstract
Sponge city planning aims to manage urban development land to prevent flooding and to support the achievement of water resource protection objectives. In this study, from the perspective of rainfall management demand and ability, we present an improved planning method, including two calculation models, aimed at determining the VCRAR (volume capture ratio of annual rainfall) and then integrating VCRAR requirements into the management of urban development land more accurately and objectively, while simultaneously considering the rainfall condition and urban planning attributes to support the implementation of sponge city planning. Compared to the current method, the VCRAR calculation model greatly improves the accuracy of the VCRAR for various space scales, and the conversion model solves the fundamental problem that urban land indicators corresponding to the VCRAR are difficult to calculate objectively and accurately. Moreover, this methodology can achieve a reasonable tradeoff between the development of individual districts and the environmental protection of the whole urban watershed, which allows a poetic vision to be turned into executable planning and design. The results of the application of this methodology in a case study in Jizhou, China, show that the improved method can make land utilization, development period and natural conditions more integrated and scientifically involved in the indicator calculation. The results also quantitatively show that the capacity of volume capture inside the site for one district increases as its green space ratio increases, and it decreases with an increase in the transformation difficulty for stormwater management facilities, when restricted by the investment and available space.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".