Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Recent Patents on Engineering

DOI: 10.2174/0118722121330025241009115648

Authors: Feng Zhao, Chuanlong Han

Institution: Suzhou University

Language: English

Topic: Environmental Quality and Pollution

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Funder: Suzhou University