Lake eutrophication prediction based on improved MIMO-DD-3Q Learning.

Area of Science:

• Environmental Science
• Data Science
• Artificial Intelligence

Background:

• Traditional single-depth prediction models struggle with the strain of time series data for lake eutrophication prediction.
• Lake eutrophication is influenced by multiple water quality factors, necessitating a comprehensive approach.

Purpose of the Study:

• To propose a novel deep reinforcement learning model for accurate lake eutrophication prediction.
• To enhance the strain capacity of prediction models for time series water quality data.
• To develop a model capable of continuous learning and optimal strategy selection for eutrophication prediction.

Main Methods:

• A deep reinforcement learning model, termed Improved MIMO-DD-3Q Learning, was developed.
• The model incorporates a greedy factor into an arctangent function and defines a mean value reward factor.
• Three Q estimates are used to update the Q table, with errors from preliminary predictions serving as secondary input for refinement.

Main Results:

• The Improved MIMO-DD-3Q Learning model demonstrated a good effect in predicting lake eutrophication.
• Analysis of multi-factor water quality data from Yongding River confirmed correlations between factors and eutrophication.
• Experimental verification validated the model's efficacy in a real-world scenario.

Conclusions:

• The Improved MIMO-DD-3Q Learning model offers a significant advancement in lake eutrophication prediction accuracy.
• The study highlights the potential of deep reinforcement learning for complex environmental time series analysis.
• The model's ability to learn and adapt makes it a promising tool for water quality management.