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What is Optimized LSTM for Water Quality Prediction about?

This paper presents a method to enhance the predictive capability of Long Short-Term Memory (LSTM) models for water quality assessment by mitigating discrepancies when applied to different datasets. It introduces genetic algorithm optimization to create hybrid models, which are then combined into ensemble models that demonstrate improved accuracy in predicting river water quality. The results indicate that the optimized ensemble models can effectively generalize across various datasets, showcasing their pot

Author

Mavione de Jesus Primo

Language

EN