Evaluation of Water Quality using Machine Learning and Deep Learning Approaches

Water quality estimation is one of the feasible and essential activities in protection of the popular health and sustainable development, which is important to any healthy ecosystem. The nonlinearity and complexity of a water system however is a major obstacle in trying to implement this objective. Such spatiotemporal complexities can be usually hard to be represented using traditional statistical models and autonomous machine learning regimens, especially in situations where there is a lot of noise or scarce information. More recent developments in the literature attempt to address the above limitations by developing integrated and hybrid deep learning frameworks that effectively trade off the advantages of state-of-the-art neural network designs such as Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN) and Transformerlike networks with complex time-frequency decomposition algorithms (e.g., Wavelet Transform, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Complete Ensemble Empirical Mode Decomposition with Adaptive No These methods improve feature recovery, replica multi-scale temporal cycles and resilience. Explainable AI and data-driven mechanistic/coupling are also helpful because they enhance forecast accuracy and interpretability. Overall, such developments present a scalable simple paradigm of dependable water quality prediction enabling intelligent surveillance, timely warning, and dependable management of the aquatic environment.