Modeling freshwater yield: deep learning applications in seawater greenhouses in Iran

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.