AI to Forecast Inflows to 30 Major Dams Nationwide Over Six-Month Horizon

The head of the Water Research Institute at the Ministry of Energy said AI does not replace field monitoring or physical models, but alongside them it can improve the accuracy and speed of data analysis.

The head of the Water Research Institute of the Ministry of Energy said: Artificial intelligence is not a substitute for field monitoring and physical models, but alongside them, it can increase the accuracy and speed of information analysis.

According to Ambassador Arjan, Kavianpour, the head of the Water Research Institute of the Ministry of Energy, in a conversation with the economic reporter of the Young Journalists Club, said: The use of artificial intelligence and data-driven methods in the management and monitoring of water resources is one of the important axes of the activities of the Water Research Institute.

He added: In the field of forecasting, the institute is developing and using artificial intelligence models to predict the inflow to the dams. Currently, the inflow to 30 important dams of the country is predicted up to a six-month horizon.

He continued: These forecasts can provide managers with valuable information for making decisions about the use of reservoirs, water resources management, electricity production planning, and preparedness against floods or water shortages.

The head of the Water Research Institute of the Ministry of Energy stated: In the field of intelligent monitoring, the goal is to combine the data obtained from the monitoring of dams and hydro-climatic variables with artificial intelligence methods so that decision-making moves from a purely reactive mode to prediction and early warning.

Kavianpour emphasized: Artificial intelligence is not a substitute for field monitoring and physical models, but alongside them, it can increase the accuracy and speed of information analysis.

Regarding this project, he said: The system for predicting dams' input was designed based on climatic indicators and historical parameters, using artificial intelligence, and calibrated in historical periods. This system is currently widely used by water resources managers.