A GIS-Based LID Framework for Sustainable Urban Runoff Management
Bibliographic record
Abstract
Low Impact Development (LID) is one of the most popular sustainable techniques for runoff reduction in urban areas. LID mimics nature by retaining or detaining the runoff at the source. Examples of LID include bioretention cells, green roofs, and porous pavements. While the primary purpose of LID is runoff reduction, several lateral benefits (environmental and socioeconomic) are accrued from LID. \n\nEven though many studies have shown the effectiveness of LID on runoff reduction, investigation around many other aspects of LID has remained limited. Out of all these aspects, there is a significant lack of a systematic decision-making model to rank LID solutions (suggest where to implement LID and what type of LID to use) to maximize the LID benefits. The objective of this dissertation is to develop an innovative simplified geospatial model (referred to as LID-Solution Evaluation and Ranking ApproacH (SERAH)) to rank the LID solutions. SERAH develops a Hydrological-Hydraulic Index (HHI) and integrates it into a Multi Criteria Decision Making (MCDM) model considering the key criteria contributing to the ranking LID solutions. \n\nIn this research, the application and effectiveness of SERAH and its corresponding indices were examined under various case scenarios and case studies (e.g., City of Toronto as the study site). Also, SERAH was validated against physical models such as HEC-HMS and PCSWMM. Further, the HHI was used for modelling climate change and urbanization scenarios for three Canadian metropolitans (Toronto, Montreal, and Vancouver).\n\nThe results of this study show that, unlike the traditional methods which use stormwater modeling for ranking LID solutions, SERAH effectively ranks LID solutions using geospatial analysis. SERAH and its corresponding indices are universally applicable since they have been deductively developed and like many similar methods are not induced and custom-built around a sample dataset. The results of this research lend themselves to the strategic planning of multifunctional sustainable infrastructures (LID); give a holistic insight about current and future demands for LID; integrate multiple disciplines (socioeconomic, environmental, geography, and hydrology) to find comprehensive sustainable solutions; and suggest a future need for similar multidisciplinary research by highlighting the gaps and limitations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".