基于人工智能的农田灌预警系统使用GA-BP神经网络在智能农业下
1School of Civil and Architectural Engineering, Harbin University, Harbin, China.
PloS one
|January 17, 2025
概括
一个增强的遗传算法-反向传播神经网络 (EGA-BPNN) 改善了智能农业灌流的预测. 这种智能系统比传统模型更准确,有助于水资源管理.
科学领域:
- 智能农业 智能农业
- 人工智能的人工智能
- 水资源管理 水资源管理
背景情况:
- 传统的逆向传播神经网络 (BPNN) 面临着局部最佳和样本依赖等挑战.
- 精确的灌流量预测对于智能农业的高效水资源管理至关重要.
研究的目的:
- 为智能田间灌预警系统开发和评估一个增强的遗传算法-反向传播神经网络 (EGA-BPNN).
- 在智能农业环境中提高灌流量预测的准确性.
主要方法:
- 使用遗传算法 (GA) 创建 EGA-BPNN 模型,优化 BPNN.
- 实施单一和双重水位流量预测模型.
- 在BPNN和EGA-BPNN之间比较预测准确度指标 (MSE,RE).
主要成果:
- 通过GA的全球搜索,EGA-BPNN克服了BPNN的本地优化和过拟合问题.
- 增加隐藏层节点减少了平均平方误差 (MSE) 和相对误差 (RE).
- EGA-BPNN实现了0.41%的平均绝对相对误差,明显超过了BPNN的1.09%.
结论:
- EGA-BPNN模型显示了灌流量的优越预测性能.
- 整合GA显著提高了BPNN模型的性能.
- 准确的预测支持智能农业中更好的水资源规划和决策.
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