Risk-informed multi-criteria assessment of dam reservoirs for floating photovoltaic farms in arid and semi-arid regions
Bibliographic record
Abstract
Floating Photovoltaic (FPV) farms have emerged as a rapidly advancing solar power technology, driven by land-use constraints and increasing global demand for sustainable energy solutions. This system has several advantages over land-based ones in terms of energy generation, water and land conservation, and environmental impacts. This study develops a risk-oriented decision-making framework to prioritize dams in Khorasan Razavi, Iran, for implementing FPV systems. To this end, the research employs the Fuzzy Analytic Hierarchy Process (FAHP) combined with risk attitudes related to multi-criteria satisfaction, to rank the selected dams. In addition, PVsyst software and the simplified Penman equation are used to analyze the potential for energy generation and evaporation reduction on these prioritized dams. In this study, three PV deployment scenarios are examined: floating PV arrays covering 1% and 10% of the reservoir’s surface area and a fixed 7 MW PV system as the feasible capacity under site-specific constraints. To guide the optimal deployment of FPV systems, this study introduces a point-based ranking framework that incorporates multiple risk perspectives—ranging from fully risk-averse to fully risk-prone scenarios. While results show consistency between the multi-criteria rankings and energy simulation outputs for several dams, notable discrepancies—such as Tabarak’s high risk-based rank despite its lower energy output and limited evaporation reduction—highlight the importance of using both approaches in tandem. The proposed framework contributes to a more comprehensive understanding of site suitability, balancing technical performance with broader contextual and stakeholder considerations.
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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 | 0.001 |
| 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 | 0.001 |
| 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".