Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine

Area of Science:

• Hydrogeology
• Artificial Intelligence
• Data Science

Background:

• Understanding groundwater dynamics is crucial for managing water resources, especially in open-pit mining areas.
• Nonlinear relationships between groundwater levels and influencing factors pose challenges for accurate prediction.
• Existing models may not fully capture the complex dynamics of groundwater systems.

Purpose of the Study:

• To investigate the nonlinear mapping relationship between groundwater levels and their influencing factors.
• To develop and evaluate an optimized prediction model for short-term groundwater dynamics.
• To compare the performance of a genetic algorithm-optimized BP neural network with traditional models.

Main Methods:

• Utilized the BP neural network model and a genetic algorithm-based BP neural network model.
• Employed the MATLAB 7 platform for model design and calculation.
• Evaluated model performance using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Nash-Sutcliffe Efficiency (NSE).

Main Results:

• The genetic algorithm optimized BP neural network model demonstrated superior performance in both training and testing phases.
• Achieved RMSE values of 0.25 (training) and 0.36 (testing).
• Achieved MAPE values of 6.7% (training) and 8.13% (testing), and NSE values of 0.87 (training) and 0.72 (testing).

Conclusions:

• The genetic algorithm-optimized BP neural network model is highly effective for short-term groundwater level prediction.
• This optimized model significantly outperforms the standard BP neural network and stepwise regression models.
• The developed model offers a promising approach for groundwater dynamic prediction with broad application prospects.