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Development of a Behavioral Model for Rural Wastewater Treatment Based on Stratified Random Incremental Sampling Algorithm

Author

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  • Ying Kang

    (Jinjiang College, Sichuan University, China)

  • Xingwang Pei

    (Xi'an University of Architecture and Technology, China)

Abstract

The increasing discharge of wastewater in rural areas has become a major environmental challenge, threatening ecological stability and rural quality of life. Effective sewage management is essential for sustainable agricultural development. As residents' environmental behaviors significantly influence pollution levels, understanding and modeling these behaviors is crucial. This study proposes an integrated framework combining a stratified random incremental sampling algorithm with an artificial neural network (ANN) to evaluate the link between rural residents' environmental actions and sewage treatment effectiveness. Simulation results show the model outperforms traditional methods, improving response speed and achieving around 93% accuracy. Under behavior-related constraints, environmental carrying capacity and ecological recovery scores also improved, indicating reduced wastewater impact. This data-driven approach offers a practical tool for enhancing rural environmental management strategies.

Suggested Citation

  • Ying Kang & Xingwang Pei, 2025. "Development of a Behavioral Model for Rural Wastewater Treatment Based on Stratified Random Incremental Sampling Algorithm," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global Scientific Publishing, vol. 16(1), pages 1-19, January.
    • Handle: RePEc:igg:jaeis0:v:16:y:2025:i:1:p:1-19
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