Bridging water demand-supply gap : through rainwater harvesting in public green spaces in Delhi, India
Bibliographic record
Abstract
"Water is likely to become one of the limiting resources of the next century, as well as one with multiple often conflicting uses." UNCEO NY1994 Presently, at least 1.1 billion of the world's people - about one in five - do not have access to safe water. City of Delhi in India is facing acute water crisis with a current demand supply gap of 236 million gallon per day (MGD) which is expected to widen to 564 MGD by the year 2021.ln view of the growing water crisis, the main objective of the thesis is to look into ways of mitigating the water crises in Delhi by bridging the water demand supply gap using environmental friendly and sustainable methods such as rainwater harvesting in the public green spaces of the region. The thesis begins with a thorough investigation of the Hydrological cycle (natural and urban), contemporary and traditional methods of rainwater harvesting, methods of recharge, and geological, hydro-geological and meteorological data of Delhi region. The regional data is overlaid and analyzed to define priority action areas and a conceptual action plan is recommended for each area. To demonstrate its feasibility, specific study areas are identified in the most stressed zone and investigated across four different scenarios ranging from neighbourhood to regional scale and from minimum to maximum intervention. It is learned through this investigation that public green spaces of Delhi hold great potential in bridging the water demand-supply gap. There are a range of modern and traditional methods available to successfully implement rainwater harvesting projects in these areas. They are technically and financially feasible and can be adopted at various levels depending on the availability of resources. It is found that public green spaces of Delhi which account for about 19% of the total urban area can bridge the water demand-supply gap by a maximum of 12.5%. Cost of the interventions can be amortized within 3 years and there are direct financial and environmental benefits to the local residents. The harvested rainwater can also be successfully used to meet the irrigation demand of the public green spaces partially, resulting in further cost savings to the government.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".