GIS- and Remote Sensing-Based Multi-Criteria Analysis for Rainwater Harvesting Site Selection: A Case Study of Wadi Sarkhar Watershed, Wasit, Iraq
Bibliographic record
Abstract
In response to escalating water demand and the depletion of natural freshwater resources, the strategic identification of suitable sites for rainwater harvesting (RWH) has emerged as a sustainable approach to mitigate water scarcity and flood risks in arid and semi-arid regions.In this study, a geospatially integrated multi-criteria decision analysis (MCDA) was conducted to delineate optimal RWH zones within the Wadi Sarkhar watershed, located in Wasit, Iraq.A total of eleven critical factors-encompassing hydrological, topographical, climatic, land use, and infrastructural parameters-were selected based on their relevance to runoff generation and storage potential.This included rainfall, runoff, evaporation, slope, land cover, soil type, proximity to roads and settlements, and stream orders (fifth to seventh).Weights were assigned to each criterion using the analytic hierarchy process (AHP), and the pairwise comparison matrix yielded a consistency ratio (CR) of 0.012, which is significantly below the accepted threshold of 0.1, indicating strong internal consistency.Spatial analysis and overlay operations were performed using ArcGIS 10.8, with the Raster Calculator employed to synthesize a final suitability map categorized into five classes: very high, high, moderate, low, and very low suitability.Results indicated that approximately 34.33% of the watershed area (435.14 km² ) was classified as having high to very high suitability for RWH interventions, while the remaining 65.67% (834.41 km² ) was deemed less favourable.A sensitivity analysis was also carried out to assess the robustness of the model by adjusting the weights of dominant criteria, confirming the model's resilience and reliability.The findings offer a robust spatial planning framework to inform policymakers, environmental engineers, and water resource managers in the development of region-specific RWH strategies that enhance water availability, reduce flood risk, and contribute to long-term ecological resilience.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".