评估地理统计方法及其与脚转移函数的整合,以估计阴子交换能力的空间分布
Farag M A Altalbawy1, Merwa Alhadrawi2,3,4, Ashok Kumar Bishoyi5
1Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia.
Environmental monitoring and assessment
|April 10, 2025
概括
准确地绘制土壤阳离子交换能力 (CEC) 地图对于可持续的土地管理至关重要. 回归Kriging与自适应神经模糊推理系统 (ANFIS) 优化相结合,显著提高了CEC预测的准确性.
科学领域:
- 土壤科学 土壤科学
- 环境科学 环境科学
- 地理空间分析是什么
背景情况:
- 阴离子交换能力 (CEC) 是一个关键的土壤特性,影响营养素的可用性和污染物流动性.
- 精确地绘制CEC的空间地图对于有效的土壤管理和环境风险评估至关重要.
研究的目的:
- 为了确定最有效的空间互叠技术来预测中国湖南省土壤CEC分布.
- 评估数据优化使用自适应神经模糊推理系统 (ANFIS) 对预测准确性的影响.
主要方法:
- 四种空间插值方法的比较:Kriging,协同Kriging,模糊Kriging和回归Kriging.
- 适应性神经模糊推理系统 (ANFIS) 的应用,以优化土壤属性数据.
- 使用确定系数 (R2),根平均平方误差 (RMSE) 和平均平方误差 (MSE) 验证模型.
主要成果:
- 回归Kriging在测试的插值方法中表现出优越的性能.
- 集成ANFIS以优化数据显著提高了回归调试的预测能力.
- 在ANFIS优化后的回归Kriging实现了R2为0.88,RMSE为2.87cmol+.kg-1和MSE为8.28cmol+.kg-1.8.
结论:
- 结合基于ANFIS的数据优化与先进的空间插值,特别是回归造,可以产生非常准确的土壤CEC预测.
- 这种综合方法提供了一种可靠的方法,用于准确地绘制土壤CEC,支持可持续的土地管理和环境保护.
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