Enhancing Water Quality using Deep Learning with VGG19 Approach
Keywords:
Convolution Neural Network, VGG19, Deep LearningAbstract
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.
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Ali, M., Prasad, R., Xiang, Y., & Yaseen, Z. M. (2020). “Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. Journal of Hydrology.”
2Deep Neural Networks for Water Quality Monitoring"
Carrasquilla, J., Konidaris, T., & Ear, E.(2015).
lizamir, M., Heddam, S., Kim, S., & Mehr, A. D. (2021).
Case studies of river and lake in USA. Journal of Cleaner Production, 285, 124868.
Wang, H.; Ma, S.; Dai, H.N. A rhombic dodecahedron topology for human-centric banking big data. IEEETrans. Comput. Soc. Syst. 2019,6, 1095–1105.
Asadollah, S. B. H. S., Sharafati, A., Motta, D., & Yaseen, Z. M. (2020). River water quality index prediction and uncertainty analysis: A comparative study of machine learningmodels. Journal of Environmental Chemical Engineering.
Alvarez-Garreton, C., Mendoza, P. A., Pablo Boisier, J., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., et al. (2018). The CAMELS-CL dataset: Catchment attributes and meteorology for large sample studies-Chile dataset. Hydrology and Earth System Sciences, 22(11), 5817–5846.
Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556. Link
Mousavi, H. S., Amin, A., Alqallaf, F., Khan, S., & Rahman, S. (2020). Water quality assessment from drone imagery using deep learning. Sustainable Cities and Society, 54, 102018. Link
A Review of Deep Learning Approaches for Environmental Sensing and Monitoring" by A. Yilmaz and V. Kisi, published in Environmental Monitoring and Assessment, 2020. Link
A Review of Deep Learning Techniques for Environmental Monitoring" by Z. Yang, M. Jiang, and W. Jia, published in Sustainability, 2020. Link
Dr. Sreedhar Bhukya, A Comprehensive Approach For Symptoms-Driven Multiple Disease Detection using Machine Learning Algorithms. ISSN:2147-67992, 2024
Dr. Sreedhar Bhukya, A Comprehensive Approach For Symptoms-Driven Multiple Disease Detection using Machine Learning Algorithms, ISSN:2147-6799, 20
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