Hydrologic performance quantification of green roofs using an analytical stochastic approach based on kernel distribution estimation: Extensive case studies in Shandong Province, northern China
Bibliographic record
Abstract
Nine representative cities in Shandong Province, northern China The analytical stochastic approach is computationally-efficient in green roofs (GRs)’ hydrologic performances. However, use of rainfall event separation methods and the resulting rainfall statistic affect the accuracy of this approach. This study integrates a Kernel distribution estimation (KDE)-based rainfall event separation method with the analytical stochastic model (ASM) developed for GRs. The proposed approach was tested for 198 design cases of GRs with considering different soil types and depths at 9 cities in the Shandong Province, northern China. Poisson and Kolmogorov–Smirnov tests can be performed to obtain the available pairs of minimum interevent time and threshold rainfall event depth. The optimal pairs of MIET- v t can be further determined based on the standardized procedure by the KDE-based rainfall event separation and characterization approach. Exponential distributions fit well the observed frequency distributions of rainfall event characteristics of the study area. ASM results using rainfall statistics obtained from a KDE-based method agree very well with those obtained from continuous simulations. The proposed integration of KDE-based rainfall statistics and ASM is accurate and useful for the planning, design and assessment of green roofs in regions of northern China. • An analytical stochastic model was integrated with the KDE-based rainfall event separation method. • Proposed model was applied for quantifying hydrologic performance of green roofs at 9 cities in Northern China. • 198 design cases considering different soil types and depths of the growing medium layer were modelled. • Results of the proposed model shows good agreement with continuous simulation results. • Larger growing medium depth achieves higher runoff reduction and lower irrigation demand.
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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 | 0.001 |
| 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 | 0.000 |
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".