Enhancing Water Quality using Deep Learning with VGG19 Approach - Donthula Mamatha (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Water quality evaluation is essential to environmental management and monitoring. Traditional methods often rely on manual inspection and It may be expensive and taking time. Deep learning has become a potent method for picture categorization in recent years. Iinvolving an evaluation of the water purity. This research VGG 19 a Convolutional water purity.Our suggestion is a channel- specifications attention gate integrates a channel-wise attention gate to focus on relevant features in the input images. Our approach includes preprocessing steps such as background elimination, removal of non-essential features, image enhancement, and noise removal to improve classification accuracy. We experimented with a collection of water surface achieving an accuracy of 97.5%, out performing previous approaches. The results demonstrate the effectiveness of deep learning models in water quality classification and highlight the importance of preprocessing techniques in improving classification performance.