Enhancement of Artificial Infiltration Capacity in Low Permeability Soils for Gaza Coastal Aquifer
Bibliographic record
Abstract
Water is a key component in determining the quality of our lives. Today, people are concerned about the quality of the water they drink. Groundwater aquifer is considered the main and the only water supply source for all kinds of human usage in the Gaza Strip which is severely deteriorated in-terms of quality and quantity in the past two decades. In the past years, several researches have been conducted on the enhancement of the role of storm water infiltration system aimed mainly at improving the quality and the availability of water. This paper investigates methods to enhance the storm water infiltration capacity of Sheikh Radwan Reservoir by using soil column pilot experiment of specific sand media and simulating the real situation using the MODFLOW groundwater flow model. The sample of storm water was collected from the reservoir in the wet season, and was allowed to infiltrate through the soil column of 170 cm depth in the soil experiment. The results of soil experiment showed that the quality of infiltrated water (BOD, COD, Suspended Solid, Ammonia) was found to conform to Palestinian standard for aquifer infiltration with 90% percent removal of contaminants, which indicate the effectiveness of the used soil media. The simulation of MODFLOW model was done considering different number and distribution of infiltration wells. The results showed an increase of water quantity in groundwater aquifer with maximum water level mound of 6 m beneath the reservoir after 3 months of flooding, and started to decrease gradually to reach -2 m to -3 m in the dry season which reflect the normal situation. The results of the study showed that 33 infiltration wells are needed to evacuate the collected storm water at the reservoir within 10 days which satisfy the function of the reservoir as flood relief and infiltration. Also it is recommended by the study to increase the sand filter depth to 2 m, in order to improve the removal percentage of contaminants to reach 100%.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".