[基于改进的BP神经网络和优化搜索算法的农业碳排放预测]
Zi-Long Su1, Wen-Liang Yan1, Hui-Min Li1
1Guangming Food Group Shanghai Farm Co., Ltd., Yancheng 224151, China.
Huan jing ke xue= Huanjing kexue
|December 4, 2024
概括
准确的农业碳排放预测对于气候目标至关重要. 一个新的神经网络模型,通过一只子搜索算法进行优化,显著提高了上海农场的预测准确性.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确的农业碳排放预测对于实现碳峰值和碳中和目标至关重要.
- 现有的农业碳排放预测方法存在重大局限性.
研究的目的:
- 开发和验证一个改进的神经网络模型,用于预测农业碳排放.
- 用上海农场的案例研究来解决当前方法的局限性.
主要方法:
- 使用排放因子方法计算农业碳排放 (2011-2021年).
- 开发了一个反向传播 (BP) 神经网络模型,将种植,畜牧和渔业的GDP作为投入.
- 使用一只子搜索算法优化了BP模型,以提高预测准确度.
主要成果:
- 优化的BP神经网络实现了96.14%的预测准确度,RMSE为12100 t·a-1和R2为0.9952.
- 与预先改进的模型相比,改进的模型显示出更高的准确性和稳定性.
- 未来的排放预测表明,畜牧业是总排放的主要贡献者.
结论:
- 用子搜索算法优化的BP神经网络为农业碳排放预测提供了高度准确和稳定的方法.
- 有效管理畜牧业规模对于减轻农业总体碳排放至关重要.
- 这种方法为制定政策提供了一个强大的工具,以实现农业碳中和.
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