Residential Low Impact Development Practices: Literature Review and Multicriteria Decision Analysis Framework for Detached Houses
Bibliographic record
Abstract
Low Impact Development (LID) is a sustainable stormwater management approach that aims to control runoff close to its source, mimicking the natural hydrological processes such as infiltration and storage. It is being adopted by many cities, where its implementation is rapidly evolving. The LID practices are small-scale measures; therefore, they need to be widely implemented to impact significantly. The selection of LIDs depends on the land use and characteristics of the area of interest. This study focuses on residential LIDs. First, a systematic and bibliometric literature review is conducted on the residential LIDs articles published up to the year 2020; a total of 94 papers were found in the Web of Science. This review resulted that LID implementation in residential areas still needs to be investigated. To assist the City, engineers, and policy-makers in implementing the suitable LIDs for detached houses, a multi-criteria decision analysis framework incorporating a hydrological model is developed in this study. The commonly used LIDs were identified, which are rain gardens, permeable pavement, rain barrels, soakaways. Seven criteria were selected – runoff depth reduction rate, peak runoff reduction rate, installation cost, maintenance cost, retrofit cost, life cycle, and aesthetical view. For the properties of the single-detached house and LIDs, the standards of Credit Valley Conservation (CVC) and Toronto and Region Conservation Authority (TRCA) were followed. The proposed decision-making framework also was applied to a case study. This framework is still in the preliminary stage, thus holds the potential to convert into a tool that will be handy enough for the homeowners and consume less time.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".