Environmental water quality prediction based on COOT-CSO-LSTM deep learning.

Area of Science:

• Environmental Science
• Hydrology
• Data Science

Background:

• Reliable water quality prediction is crucial for effective water resource management.
• Dissolved oxygen (DO) is a key water quality metric requiring accurate forecasting.
• Watershed systems present challenges due to data nonstationarity, unpredictability, and nonlinearity.

Purpose of the Study:

• To propose an enhanced hybrid model for improved water quality prediction, focusing on DO.
• To leverage the COOT optimization method to overcome data complexities in water quality parameters.
• To provide a robust framework for predicting water quality in river systems.

Main Methods:

• An enhanced Long Short-Term Memory (LSTM) model was developed.
• A hybrid model integrated LSTM with the COOT (Coot Bird Optimizer) meta-heuristic optimization technique.
• The COOT method, inspired by coot bird flocking behavior, optimizes LSTM parameters.

Main Results:

• The hybrid model demonstrated improved performance in predicting water quality metrics.
• The model effectively handled the nonstationarity, unpredictability, and nonlinearity of water quality data.
• Weekly water quality data from the Vaigai River, India, was successfully tested.

Conclusions:

• The proposed hybrid LSTM-COOT model offers a promising alternative for water quality prediction.
• This approach can enhance watershed management strategies and pollution control efforts.
• The model provides a foundation for future basin-wide water quality management initiatives.