Optimization Analysis of Urban and Rural Environmental Planning Based on Artificial Intelligence and Intelligent Information Processing Algorithms

Introduction: With rapid economic development and urbanization, urban and rural areas face environmental challenges. Traditional optimization methods struggle with complexity and often fail to find global optima. Methods: This study integrates a Bidirectional Long Short-Term Memory Network (BiLSTM) with Genetic Algorithm (GA)-Ant Colony Optimization (ACO) to improve environmental planning. BiLSTM captures long-term data correlations and predicts future trends, achieving an average Mean Squared Error (MSE) of 0.0217. GA-ACO, using GA-generated solutions as initial input for ACO, identifies optimal planning solutions. Results and Discussion: This approach enhances air quality indicators and provides robust predictions and optimizations for sustainable urban and rural development. Conclusion: To sum up, future development needs comprehensive technical progress, policy support and public participation to form a multi-level and multi-field collaborative mechanism to achieve the real sustainable development goal.