通过随机森林算法预测不同作物种的化物含量
Yuqi Zhang1, Jie Luo1, Siyao Feng1
1College of Resources and Environment, Yangtze University, 111 University Road, Wuhan, China.
Environmental geochemistry and health
|September 9, 2024
概括
本研究引入了一种随机森林模型,使用土壤地质化学数据准确预测农作物中的化物 (F) 含量. 该方法通过克服传统土壤分析的局限性,提高了农业区分和作物质量评估.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 地质化学 地质化学
背景情况:
- 化物 (F) 是一种必不可少的微量元素,但其在环境中的过量或不足会给健康带来风险.
- 伪总土壤F含量与植物F水平的相关性很差;生物可用F是一个更好的指标.
- 传统的土壤调查对农业区划缺乏准确性,原因是植物特定的F积累和吸收变化.
研究的目的:
- 研究影响不同作物的化物生物积累系数的因素.
- 使用地质化学调查数据,开发作物化物含量的预测模型.
- 建立一个新的方法框架,以提高农业质量和效率.
主要方法:
- 利用1:50,000尺度的土壤地质化学调查数据,来自中国的新宁-莱杜地区.
- 应用随机森林 (RF) 算法,从29个土壤参数预测作物F含量.
- 选择了可生物利用的P,可生物利用的Zn,可浸的Pb和Sr作为关键预测指标.
主要成果:
- 与多变量线性回归 (MLR) 相比,RF模型在作物化物含量预测准确度上实现了95.23%的改进.
- 射频模型在部分最小平方回归 (PLSR) 上表现出卓越的准确性和稳定性.
- 成功实现了跨物种F生物积累研究,并最大限度地利用了地化学数据.
结论:
- 开发的RF模型提供了一种可靠和有效的方法来预测作物化物含量.
- 这种方法通过提供准确的植物F水平估计,提高了农业的区分和质量管理.
- 为优化作物生产和减轻潜在的化物相关风险提供了一个新的框架.
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