探索土壤 (Rn) 度及其与地质和气象因素的联系
Ahmad Muhammad1, Salim Jibrin Danbatta2, Ibrahim Yahaya Muhammad3
1Department of Physics and Material Science, College of Arts and Sciences, Qatar University, P. O. Box 2713, Doha, Qatar.
Environmental science and pollution research international
|November 27, 2023
概括
本研究引入了一种混合多回归和人工神经网络 (ANN) 模型,以利用气象数据预测土壤 (Rn) 的变化. 该模型有助于理解石化层与大气之间的相互作用,并改善大气研究.
科学领域:
- 地质物理学和大气科学 大气科学
- 环境监测和环境建模
背景情况:
- 土壤 (Rn) 度受到石化层和大气之间的复杂相互作用的影响.
- 对变量的准确建模对于大气研究和了解地球系统过程至关重要.
- 由于影响水平的众多物理变量,现有的模型面临局限性.
研究的目的:
- 根据气象参数开发一个可靠的模型来预测土壤变化.
- 通过结合土壤动态来增强大气模型的现实性.
- 调查模型性能异常和地震事件之间的潜在联系.
主要方法:
- 开发一种混合模型,多ANN,结合多回归和人工神经网络 (ANN) 技术.
- 训练和测试模型使用70%和30%的收集数据,分别.
- 通过蒙特卡洛模拟验证模型的稳定性.
主要成果:
- 多ANN模型展示了从气象数据中预测子度的能力.
- 识别具有较低模型性能的区域,可能与地震活动相关.
- 该模型提供了一种方法来确定地震-离子体合的下限条件.
结论:
- 混合多ANN模型为预测土壤变化提供了一个有希望的方法.
- 这种预测能力可以显著改善大气和地层相互作用研究.
- 该模型是复杂合模型中设置边界条件的有价值工具.
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