Monitoring and Management of Aquatic Ecosystems using Deep Learning

Water quality (WQ) is essential to maintain environmental preservation, agricultural sustainability, and public health. Conventional approaches to water quality analysis can entail time-consuming and labor-intensive chemical and biological evaluations. However, by utilizing automated and scalable methods, Machine Learning (DL) breakthroughs have completely changed the capacity to examine water quality more effectively. This paper presents the hybrid machine learning method LR2FXGB (Logistic Regression and Random Forest with XGBoost) model to assess the water quality. It combines the Logistic Regression for probability estimation, Random Forest for feature selection, and XG Boost for final prediction optimization. It also compares the effectiveness of the proposed model with six different techniques for estimating water quality. The publicly accessible dataset is used to show the model's performance, preprocessing techniques, and performance indicators employed in this work. The results on the training and testing dataset among six techniques proposed method gives 97% accuracy. Finally, a roadmap for further study is provided by discussing new trends such as transformer structures, federated learning, and multimodal techniques.