Site suitability analysis for the construction of water reservoirs in drought‐prone areas of Bangladesh using geospatial techniques
Bibliographic record
Abstract
Abstract Drought is becoming a common phenomenon in the northwestern (NW) region of Bangladesh. To address this problem, the government has taken multiple initiatives such as the construction of deep tube wells, re‐activation of abandoned deep tube wells, re‐excavation of canals, construction of cross dams, and digging wells to hold water during monsoon. However, the construction of medium‐ to large‐sized water reservoirs is somewhat overlooked or missed, which can hold large quantities of surface water and, at the same time, can recharge groundwater. Finding suitable sites for water reservoirs is a challenge. The present study aims to fulfill these gaps. We have applied a spatial multi‐criteria technique called the analytical hierarchy process (AHP) to identify suitable sites for water reservoir construction in the drought‐prone NW region of Bangladesh. A total of 12 criteria such as settlement, land use, slope, soil, groundwater depth, road network, river network, vegetation cover, rainfall, geology, protected areas, and aquifer depth were selected. The study shows that 17 percent of the area is highly suitable for reservoir construction, followed by 24 percent moderately suitable and 25 percent marginally suitable. In contrast, approximately 30 percent of the area is unsuitable for reservoir construction. Among 16 districts of northwest region, areas of Rangpur district are mostly suitable (30 percent), followed by Gaibandha district (16 percent). Construction of water reservoir in the identified areas will lower irrigation water pumping costs and will raise groundwater levels in land adjacent to the reservoir in dry season.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".