利用非线性数据分析技术监测恒河水质

B.D.K. Patro, Shivam Sharma, Abhishek Bajpai
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引用次数: 0

摘要

恒河是印度最大、最重要的河流之一,数百万人的健康和福祉依赖于河水的纯净。传统的线性模型被广泛用于评估水质,但在捕捉水质因素之间复杂的非线性相互作用方面存在局限性。另一方面,非线性数据分析能够识别这些联系,并可以提供更精确和可信的水质估计。为了监测恒河的水质,本研究提出了一种非线性数据分析方法,需要收集和研究大量的水质数据。结果表明,所提出的方法优于传统的线性模型,可以为恒河水质提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Water Quality Monitoring of River Ganga Using Non-Linear Data Analytics
The Ganga River is one of India's biggest and most significant rivers, and the health and welfare of millions of people depend on the purity of its water. The traditional linear models that have been used extensively to assess water quality have limitations in their ability to capture the intricate non-linear interactions between the water quality factors. On the other hand, non-linear data analytics are able to identify these linkages and can offer more precise and trustworthy estimates of water quality. In order to monitor the River Ganga's water quality, this study suggests a non-linear data analytics approach that entails gathering and studying a significant amount of water quality data. The results show that the proposed approach outperforms traditional linear models and can provide valuable insights into the water quality of the River Ganga.
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