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在一个关键农业地区灌水质的预测建模和空间分析:基于ANN的方法
Deepali Goyal1, A K Haritash1, S K Singh1
1Department of Environmental Engineering, Delhi Technological University, Delhi, India.
概括
印度卢迪安纳的地下水质量显示出大量的盐度和度问题,这对灌有很大影响. 一个优化的模型准确地预测灌水质量指数 (IWQI),帮助可持续的水资源管理.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 水资源管理 水资源管理
背景情况:
- 地下水质量对于灌至关重要,影响作物产量和土壤健康.
- 评估灌水的适用性对于可持续农业和粮食安全至关重要.
研究的目的:
- 评估印度旁遮普邦卢迪安纳市地下水是否适合用于灌目的.
- 分析盐度和危害,并确定整体灌水质量指数 (IWQI).
主要方法:
- 分析地下水样本 (n=152) 的电导率 (EC),%Na,吸附比率 (SAR) 和Piper (PI) 值.
- 使用EC,SAR,Na+,Cl-和HCO3-值计算灌水质量指数 (IWQI).
- 使用IBM SPSS进行IWQI预测的人工神经网络 (ANN) 模型的开发,通过根平均平方误差 (RMSE) 验证.
主要成果:
- 62.5%的样本显示中等盐度危险 (250-750μS/cm),其中37.5%显示高盐度危险.
- 大多数样本被分类为优秀到良好的%Na和SAR的低度.
- 很大一部分地下水样本 (21.7%严重,37.5%高) 表示基于IWQI的灌使用限制.
结论:
- 卢迪安纳区的地下水由于盐度和度对灌提出了相当大的挑战.
- 开发的ANN模型准确地预测IWQI,为未来的水质评估提供了宝贵的工具.
- 调查结果支持可持续的水资源管理和为该地区的农业实践做出明智的政策决策,与可持续发展目标6保持一致.
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