Machine Learning and Deep Learning-Based Feasibility Study of Persian Gulf to Tehran Water Transfer

Tehran is facing an escalating water crisis driven by rapid population growth, declining groundwater reserves, and long-term climatic stress. These pressures have created an urgent need for unconventional and sustainable water-supply strategies. One of the most ambitious proposals is the large-scale transfer of desalinated water from the Persian Gulf to Tehran. Although the project requires substantial energy, advanced infrastructure, and significant capital investment, its potential to enhance long-term water security makes its assessment essential. This study evaluates the feasibility of the Persian Gulf–Tehran water-transfer project using advanced Machine Learning (ML) and Deep Learning (DL) models to provide a more accurate and data-driven analysis than traditional engineering approaches. A comprehensive dataset—including climatic variables, elevation profiles, pipeline behavior, energy loads, desalination efficiency, and cost parameters—was analyzed using algorithms such as Random Forest, XGBoost, LSTM networks, and convolutional neural networks. These models were employed to forecast pumping requirements, simulate pressure fluctuations, predict operational risks, and assess system stability under future climate scenarios. Results indicate that DL architectures substantially improve the accuracy of predicting energy losses, pressure variations, and seasonal operational patterns. Furthermore, scenario-based analysis shows that, despite its high cost, the project remains technically feasible and strategically valuable when supported by optimized pumping schedules, renewable-energy integration, and adaptive environmental management. A cost assessment shows that the final all-in price of delivering desalinated water to Tehran is approximately 3–4 USD/m³ under normal conditions, and 6–9 USD/m³ under severe U.S. sanctions, including desalination, ~1,200 km of pumping, 1,200 m elevation lift, O&M, environmental mitigation, and levelized capital costs. Overall, the study concludes that, while economically intensive, the project represents a strategically significant option for ensuring Tehran’s long-term water security.

کلیدواژه ها:

Keywords: Machine Learning ، Deep Learning ، Feasibility Study ، Persian Gulf ، Tehran Water Transfer