Artificial Neural Network (ANN) Water-Level Prediction Model as a Tool for the Sustainable Management of the Vrana Lake (Croatia) Water Supply System

With climate change and increasing summer tourism in Croatia, the protection and sustainable management of natural freshwater resources, such as lakes, are becoming crucial. This research aims to develop a predictive hydrological model that can forecast water levels in Vrana Lake to serve as a tool for the sustainable management of water supply systems. Therefore, in this paper, a data-driven predictive model based on an artificial neural network (ANN) is implemented. For this purpose, the multilayer perceptron (MLP) ANN architecture is chosen. For model development, the monthly data of rainfall amount, evaporation, losses, water supply pumping, and lake water levels at Vrana Lake from the years 1954-2022 were used, and the model for water level prediction is developed for time prediction steps: (i) Delta t = 1 month, (ii) Delta t = 2 months, (iii) Delta t = 4 months, and (iv) Delta t = 6 months. The model quality assessment indicated strong prediction capabilities for time steps of Delta t = 1 month and Delta t = 2 months. However, the models for time steps of Delta t = 4 months and Delta t = 6 months exhibited lower quality. Despite this, they can still serve as valid indicators for predicting trends in water level fluctuations.