Forecasting River Water Levels Influenced by Hydropower Plant Daily Operations using Artificial Neural Networks

Abstract

Multipurpose water systems are used to deal with multiple objectives related to the usage of water for daily human activities. These activities are often conflicted which creates a challenging water management task. To provide reliable water resources management decision support tools for successful forecasting of hydraulic data (river flows and water levels) are essential. This research presents an approach for forecasting river water levels influenced by hydropower plant operations using artificial neural networks. This approach estimates hourly water level fluctuations at the control location using the water levels and hydropower plant discharge data as input. This tool can be used for fast assessment of different hydropower plant operation plans and help in choosing the optimal one. This water level forecasting procedure is applied and tested on the Iron Gate water system, placed on the Danube River, to deal with multiple objectives in water system management (hydropower production, flood protection, and inland navigation) and shows promising results.