Application of Artificial Neural Networks for Optimizing Coordinated Development between Agriculture and Logistics in Zhejiang Province: A Case Study on Rural Revitalization Strategies | ||
| Journal of Agricultural Science and Technology | ||
| Article 1, Volume 27, Issue 3, 2025, Pages 497-511 PDF (9.92 M) | ||
| Document Type: Original Research | ||
| DOI: 10.22034/JAST.27.3.497 | ||
| Authors | ||
| Weiping Wang* 1; Youcheng Shan1; Jianping Jing2 | ||
| 1School of Logistics and Supply Chain Management, Zhejiang Vocational and Technical College of Economics, Hangzhou, China, 3100182. | ||
| 2College of Foreign Languages, Xinjiang Agricultural University, Urumqi, China, 830091 | ||
| Abstract | ||
| This study applies Artificial Neural Networks (ANNs) to assess the impact of climate factors on the collaborative development of agriculture and logistics in Zhejiang, China. The ANN model investigates how average temperature and rainfall from 2017-2022 influence crop yield, water usage, energy demand, logistics efficiency, and economic growth at yearly and seasonal scales. By training the neural network using temperature and rainfall data obtained from ten weather stations, alongside output indicators sourced from statistical yearbooks, the ANN demonstrates exceptional precision, yielding an average R2 value of 0.9725 when compared to real-world outputs through linear regression analysis. Notably, the study reveals climate-induced variations in outputs, with peaks observed in crop yield, water consumption, energy usage, and economic growth during warmer summers that surpass historical norms by 1-2°C. Furthermore, the presence of subpar rainfall ranging from 20-30 mm also exerts an influence on these patterns. Seasonal forecasts underscore discernible reactions to climatic factors, especially during the spring and summer seasons. The findings underscore the intricate relationship between environmental and economic factors, indicating progress in agricultural practices, with vulnerability to short-term climate fluctuations. The study emphasizes the necessity of adapting supply management to address increased water demands and transitioning to clean energy sources due to rising energy consumption. Moreover, optimizing logistics requires strategic seasonal infrastructure planning. | ||
| Keywords | ||
| Agriculture-logistics systems; Artificial intelligence modeling; Climate-economic linkages; Rural sustainability; Temporal pattern recognition | ||
| References | ||
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