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 powertechnology, driven by land-use constraints and increasing global demand forsustainable energy solutions. This system has several advantages over land-basedones in terms of energy generation, water and land conservation, and environmentalimpacts. This study develops a risk-oriented decision-making framework to prioritizedams in Khorasan Razavi, Iran, for implementing FPV systems. To this end, theresearch employs the Fuzzy Analytic Hierarchy Process (FAHP) combined withrisk attitudes related to multi-criteria satisfaction, to rank the selected dams. Inaddition, PVsyst software and the simplified Penman equation are used to analyzethe potential for energy generation and evaporation reduction on these prioritizeddams. In this study, three PV deployment scenarios are examined: floating PVarrays covering 1% and 10% of the reservoir’s surface area and a fixed 7 MW PVsystem as the feasible capacity under site-specific constraints. To guide the optimaldeployment of FPV systems, this study introduces a point-based ranking frameworkthat incorporates multiple risk perspectives—ranging from fully risk-averse to fullyrisk-prone scenarios. While results show consistency between the multi-criteriarankings and energy simulation outputs for several dams, notable discrepancies—such as Tabarak’s high risk-based rank despite its lower energy output and limitedevaporation reduction—highlight the importance of using both approachesin tandem. The proposed framework contributes to a more comprehensiveunderstanding of site suitability, balancing technical performance with broadercontextual and stakeholder considerations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".