模拟使用人工神经网络在滴水制造系统下沿滴水侧面的发射器放电
Oluwaseun Temitope Faloye1,2, Smart Idumoro Samuel3,4, Abiodun Afolabi Okunola3,4
1Department of Water Resources Management and Agrometeorology, Federal University, Oye-Ekiti, Ekiti, Nigeria.
PloS one
|July 8, 2025
概括
人工神经网络 (ANN) 准确地预测滴水制造系统中的发射器排放. 这种模型通过模拟水和营养物质的分布来简化灌管理,减少了繁的田间测量.
科学领域:
- 农业工程 农业工程
- 水文学 水文学
- 人工智能的人工智能
背景情况:
- 精确的排放器排放模拟对于优化滴水制造系统中的水和营养分配至关重要.
- 排放的现场测量是耗时和劳动密集的,需要开发高效的预测模型.
- 人工神经网络 (ANN) 的应用用于模拟沿侧面的滴滴发射器排放,代表了灌研究中的新方法.
研究的目的:
- 使用人工神经网络 (ANN) 模型,预测沿滴水侧面的发射器放电.
- 评估开发的滴灌系统ANN模型的性能和准确性.
主要方法:
- 开发了一个人工神经网络 (ANN) 模型来模拟发射器放电.
- 输入参数包括管道长度,升高头和横向发射器距离.
- 现场测量的排放数据作为模型培训和验证的输出.
主要成果:
- 该ANN模型在模拟发射器放电时表现出高准确度和精度,R2值在0.81到0.89之间.
- 正常化根平均平方误差 (NRMSE) 始终低于20%,表明预测性能良好.
- 管道长度和升高头部之间的观察到的相互作用影响了统一系数 (CU) 和发射器排放.
结论:
- 开发的ANN模型是高效和有效的,用于模拟滴滴制造系统内的排放器排放的变化.
- 该模型为改善农业灌系统的设计和管理提供了有价值的工具.
- ANN应用程序为监测滴灌性能提供了传统,劳动密集型方法的有希望的替代方案.
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