Akademik Veri Yönetim Sistemi
WATER, sa.5, 2025 (SCI-Expanded)
Population growth, industrialization, excessive energy consumption, and deforestation have led to climate change and affected water resources like dams intended for public drinking water. Meteorological parameters could be used to understand these effects better to anticipate the water quality of the dam. Artificial neural networks (ANNs) are favored in hydrology due to their accuracy and robustness. This study modeled climatic effects on the water quality of Do & gbreve;anc & imath; dam using a feed-forward neural network with one input, one hidden, and one output layer. Three models were tested using various combinations of meteorological data as input and Do & gbreve;anc & imath; dam's water quality data as output. Model success was determined by the mean squared error and correlation coefficient (R) between the observed and predicted data. Resilient back-propagation and Levenberg-Marquardt were tested for each model to find an appropriate training algorithm. The model with the least error (1.12-1.68) and highest R value (0.93-0.99) used three meteorological inputs (air temperature, global solar radiation, and solar intensity), six water quality parameters of Do & gbreve;anc & imath; dam as output (water temperature, pH, dissolved oxygen, manganese, arsenic, and iron concentrations), and ten hidden nodes. The two training algorithms employed in this study did not differ statistically (p > 0.05). However, the Levenberg-Marquardt training approach demonstrated a slight advantage over the resilient back-propagation algorithm by achieving reduced error and higher correlation in most of the models tested in this study. Also, better convergence and faster training with a lesser gradient value were noted for the LM algorithm. It was concluded that ANNs could predict a dam's water quality using meteorological data, making it a useful tool for climatological water quality management and contributing to sustainable water resource planning.