气象参数对印度加尔各答土壤的影响:使用机器学习技术进行调查
Arindam Kumar Naskar1,2,3, Javed Akhter4, Mahasin Gazi1,2,5
1Nuclear and Particle Physics Research Centre, Department of Physics, Jadavpur University, Kolkata, 700032, West Bengal, India.
Environmental science and pollution research international
|September 14, 2023
概括
在加尔各答的土壤活动因季节而异,在夏季和季风中达到顶峰. 土壤温度与水平有很强的相关性,而像梯度增强机 (GBM) 这样的机器学习模型准确地预测了这些变化.
科学领域:
- 环境科学 环境科学
- 地质物理学 地质物理学
- 大气科学 大气科学
背景情况:
- 土壤活动是一个重要的环境参数,受到各种因素的影响.
- 了解这些影响对于环境监测和危险评估至关重要.
- 持续监测为分析时间变化提供了有价值的数据.
研究的目的:
- 在印度加尔各答持续测量一年的每日土壤活动.
- 分析土壤活动与大气参数 (如温度,压力,湿度和降雨量) 之间的相关性.
- 根据这些参数,评估机器学习模型在预测土壤水平方面的性能.
主要方法:
- 使用BARASOL BMC2探头,每天连续测量土壤活动.
- 分析大气参数,包括土壤温度,土壤压力,湿度,空气温度和降雨量.
- 机器学习算法的应用:主要组件回归 (PCR),支向量回归 (SVR),随机森林回归 (RF) 和梯度增强机器 (GBM).
主要成果:
- 土壤水平呈现季节性变化,冬季活动最小,夏季和季风活动较高.
- 土壤温度与土壤活动的正相关性最强,而最大湿度的正相关性最小.
- 暴雨事件导致土壤水平大幅降低.
- 与PCR和SVR相比,渐变增强机 (GBM) 和随机森林回归 (RF) 在预测土壤活性方面表现优越.
结论:
- 季节性和大气因素显著影响土壤活动.
- 机器学习模型,特别是GBM,提供了可靠和准确的方法来预测土壤水平.
- 这些发现有助于更好地了解土壤动态和环境监测.
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