Water qualitypredictionartificial neural networkrecurrent neural networklong short term memoryclassification accuracyDischarge of untreated waste water, municipal sewage, industrial effluents, dumping of degradable and non-degradable wastes has polluted natural water sources like river, lake, pond to a great ...
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduou
The study utilizes a combination of a CNN, CRNN, and M5 Model Tree (CNN-CRNN-M5T) to predict water quality parameters. The study uses advanced machine learning techniques such as CNNs, clockwork RNNs, and M5 Model Trees to enhance water quality parameter prediction accuracy. Environmental ...
Environmental water quality prediction based on COOT-CSO-LSTM deep learning Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolv... S Rajagopal,SS Ganesh,A Karthick,... - 《Environmental Science & ...
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Machine learning algorithms for efficient water quality prediction 2022, Modeling Earth Systems and Environment Prediction of irrigation water quality indices based on machine learning and regression models 2022, Applied Water Science Prediction of irrigation groundwater quality parameters using ANN, LSTM, an...
The water quality prediction performance of machine learning models may be not only dependent on the models, but also dependent on the parameters in data set chosen for training the learning models. Moreover, the key water parameters should also be identified by the learning models, in order to...
To achieve this, our proposed system uses a lightweight Spatially Shared Attention Long Short-Term Memory (SSA-LSTM) model that captures both temporal and spatial dependencies of DO content in water, enabling accurate prediction of hypoxia conditions. Our model outperforms traditional LSTM models and...
Deep learning (DL) has been progressively used in water quality retrieval because it efficiently captures the potential relationship between target variables and imagery. In this study, the multimodal deep learning (MDL) models were developed and rigorously validated using atmospherically corrected Landsat...
Another solution for monitoring water quality is predictive modelling using machine learning and deep learning approaches. Compared to other conventional methods, it has several advantages: lower costs, efficient in terms of time required for travel and collection, enables prediction under various phases ...