根据印度德里大都会的ANN模型开发地下水质量指数
Abdul Gani1, Mohit Singh1, Shray Pathak2
1Department of Civil Engineering, Netaji Subhas University of Technology, New Delhi, 110073, India.
Environmental science and pollution research international
|December 22, 2023
概括
人工神经网络 (ANN) 有效地评估地下水质量,识别德里污染地区进行整治. 这种人工智能方法通过确定污染热点并指导清理工作,确保更安全的饮用水.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 人工智能的人工智能
背景情况:
- 地下水是全球饮用水的重要来源,但由于人口增长,工业化和人类活动,其质量正在下降.
- 地形和排水变化进一步降低地下水的数量和质量,需要污染风险评估和缓解策略.
- 水质指数 (WQI) 模型模拟水质,人工神经网络 (ANN) 为特定地点提供高效和准确的WQI开发.
研究的目的:
- 开发一种使用人工神经网络 (ANN) 来描述印度德里大都会城市地下水质量的新方法.
- 确定地下水质量的地理差异,并确定需要修复的受污染区域.
- 确保地下水适用于住宅和饮用目的.
主要方法:
- 利用人工神经网络 (ANN) 开发水质指数 (WQI) 和评估地下水质量.
- 分析了德里各地地下水质量的地理变化.
- 与训练和测试阶段观察到的数据相关联的ANN模型预测.
主要成果:
- 在训练过程中,ANN模型表现出高效率 (R值为98.10%) 和在测试过程中表现出非常高的准确性 (R值为99.99-100%).
- 确定了WQI的显著变化,在贾加特普尔最低为41.51,在佩拉加希最高为779.01.
- 确定了有污染地下水的特定地点,需要有针对性的清理工作.
结论:
- 人工智能,特别是ANN,为管理和改善地下水质量提供了强大而有效的工具.
- 该研究成功地描述了德里的地下水质量,为修复和确保水安全提供了路线图.
- 这些发现突显了先进的人工智能技术在可持续地下水资源管理方面的潜力.
更多相关视频
09:33Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
Published on: December 23, 2022
2.2K
10:53Concentration of Virus Particles from Environmental Water and Wastewater Samples Using Skimmed Milk Flocculation and Ultrafiltration
Published on: March 17, 2023
1.6K
相关概念视频
Quality of Water
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
Testing Water Quality
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
