预测土壤圆指数和评估风能和太阳能发电场发展的适宜性,在使用机器学习技术的情况下
1Electrical and Control Department, Arab Academy for Science and Technology, Cairo, 11799, Egypt. eng_marwa@aast.edu.
Scientific reports
|February 5, 2024
概括
这项研究使用机器学习准确预测土壤紧缩. XGBoost模型显示出卓越的性能,有助于可持续农业和可再生能源土地评估.
科学领域:
- 农业科学 农业科学
- 土壤科学 土壤科学
- 数据科学数据科学数据科学
背景情况:
- 土壤紧缩是影响农业生产率和土地适用性的关键因素.
- 准确预测土壤紧缩对于明智地进行土地管理决策至关重要.
- 现有的方法可能无法完全捕捉土壤参数和凝结之间的复杂关系.
研究的目的:
- 开发和评估一种用于预测土壤紧缩的新型机器学习方法.
- 通过使用土壤指数数据,确定最有效的人工智能 (AI) 技术来预测土壤紧缩.
- 为农业和可再生能源项目展示准确的土壤紧缩预测的实际含义.
主要方法:
- 使用支持向量回归 (SVR) 来处理输入土壤参数.
- 应用梯度增强 (XGBoost),决策树,人工神经网络和自适应神经模糊推理系统.
- 使用诸如平均平方误差和相关系数等指标评估模型性能.
主要成果:
- XGBoost模型在预测土壤紧缩方面表现出卓越的准确性和可靠性.
- 与其他AI技术相比,XGBoost实现了较低的平均平方误差和高的相关系数.
- 该研究成功地将SVR与其他机器学习模型集成,以提高预测能力.
结论:
- XGBoost模型对预测土壤紧缩非常有效,为土壤科学提供了可靠的工具.
- 准确的土壤紧缩预测支持更好的土壤管理,提高农业生产力,并为土地适用性评估提供信息.
- 这种人工智能驱动的方法为土地利用规划,可持续农业和可再生能源项目评估提供了宝贵的见解.
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