Water Recharges Suitability in Kabul Aquifer System within the Upper Indus Basin
Bibliographic record
Abstract
Groundwater is the main source of water for drinking, household use, and irrigation in Kabul; however, the water table is dropping due to the excessive extraction over the past two decades. The groundwater restoration criteria selection mainly depends on the techniques used to recharge the aquifer. The design of infiltration basins, for example, requires different technical criteria than the installation of infiltration wells. The different set of parameters is relevant to water being infiltrated at the surface in comparison with water being injected into the aquifers. Restoration of the groundwater resources are complicated and expensive tasks. An inexpensive preliminary investigation of the potential recharge areas, especially in developing countries such as Afghanistan with its complex Upper Indus River Basin, can be reasonably explored. The present research aims to identify the potential recharge sites through employing GIS and Analytical Hierarchy Process (AHP) and combining remote sensing information with in situ and geospatial data obtained from related organizations in Afghanistan. These data sets were employed to document nine thematic layers which include slope, drainage density, rainfall, distance to fault, distance to river channel, lithology, and ground water table, land cover, and soil texture. All of the thematic layers were allocated and ranked, based on previous studies, and field surveys and extensive questionnaire surveys carried out with Afghan experts. Based on the collected and processed data output, the groundwater recharge values were determined. These recharge values were grouped into four classes assessing the suitability for recharge as very high (100%), high (63%), moderate (26%), and low (10%). The relative importance of the various geospatial layers was identified and shows that slope (19.2%) is the most important, and faults (3.8%) the least important. The selection of climatic characteristics and geological characteristics as the most important criteria in the artificial recharge of the aquifer are investigated in many regions with good access to data and opportunities for validation and verifications. However, in regions with limited data due to the complexities in collecting data in Afghanistan, proper researching with sufficient data is a challenge. The novelty of this research is the cross-disciplinary approach with incorporation of a compiled set of input data with the set of various criteria (nine criteria based on which layers are formed, including slope, drainage density, rainfall, distance to fault, distance to river channel, lithology, ground water table, land cover, and soil texture) and experts’ questionnaires. The AHP methodology expanded with the cross-disciplinary approach by adding the local experts´ questionnaires survey can be very handy in areas with limited access to data, to provide the preliminary investigations, and reduce expenses on the localized expensive and often dangerous field works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".