URBAN DEVELOPMENT AND ITS RELATIONSHIP WITH THE WATER CRISIS OVER THE NEXT TWENTY YEARS (2042) IN THE CITY OF ZANJAN, IRAN
Bibliographic record
Abstract
Today, urban settlements are at risk of water shortage for various uses due to climate change and rapid population growth.Although this crisis has been considered in urban studies from different aspects, the future effects of urban development patterns on urban water shortage have rarely been investigated.This research aims to identify areas prone to the development of Zanjan city, emphasizing the indicators of the water crisis and the supply of drinking water to the city's population in the future.Currently, the underground water aquifers of Zanjan city supply 46% of its drinking water.In addition, due to the decrease in rainfall and increase in evaporation and air temperature (climate changes), the amount of surface water entering the Teham dam is decreasing.Consequently, Zanjan city becomes gradually dependent on underground water sources.In this research, the population growth trend was studied using the data obtained from the official reports of the Census Center of Iran.Also, the Canadian Water Quality Index (CWQI) method was used to analyze hadrochemical data, and interpolation methods were used in the GIS environment to draw quantitative and qualitative water maps.Based on the maps of the quality, depth, and direction of the underground water flow in the city and its surrounding areas, it is suggested that the western areas are the best place for the city's expansion.Moreover, the best place in the western regions, especially the northwest, was determined to be more suitable for the city's expansion.Overall, the region in the northwest and the model of linear city development were chosen as the optimal region and model, respectively.This decision was made according to the current direction of development and topographical conditions, the analyses performed, and issues related to the water crisis in Zanjan city.
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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 | 0.000 | 0.000 |
| 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".