Modeling freshwater yield: deep learning applications in seawater greenhouses in Iran
Bibliographic record
Abstract
The seawater greenhouse (SWGH) is an environmentally friendly solution that utilizes solar-driven desalination techniques to produce freshwater while simultaneously creating a controlled agricultural environment. Integrating SWGH with green buildings optimizes sustainability by reducing dependence on conventional water supplies and lowering carbon emissions. This study develops a deep learning-based predictive model to optimize freshwater production in SWGHs, particularly in the Makran region. The Makran coast faces severe freshwater shortages due to its arid climate, limited groundwater resources, and growing agricultural demand. SWGH technology is particularly suitable for this region, leveraging abundant solar radiation and seawater to sustainably generate freshwater while enhancing agricultural productivity and environmental resilience. This study aims to develop a deep learning-based predictive model to forecast freshwater production in SWGHs for integration into green building frameworks. The model forecasts freshwater yield by analyzing environmental, especially climate and operational parameters. A two-stage deep learning-based prediction approach was employed, utilizing CNN-LSTM, BiLSTM, BiGRU, CNN-GRU, and MLP models. First, global horizontal irradiance (GHI) was predicted as a primary factor influencing SWGH performance. Then, freshwater production was estimated using predicted solar radiation. Among tested models, CNN-LSTM achieved the highest accuracy with achieving a R2 of 0.9727, a RMSE of 0.0021, and a MSE of 0.0022. The freshwater production rate was predicted per unit area, and the average annual yield for 2024–2033 was estimated at 1454.25 L/m². The results confirm SWGH as a viable solution for sustainable water management in arid coastal regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".