Conductivity Classification Using Machine Learning Algorithms in the "Bramianon" Dam

被引：0

作者：

Nichat, Kiourt ^{[1
]}

Iliadis, Lazaros ^{[1
]}

Papaleonidas, Antonios ^{[1
]}

机构：

[1] Democritus Univ Thrace, Dept Civil Engn, Sch Engn, Xanthi, Greece

来源：

24TH INTERNATIONAL CONFERENCE ON ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EAAAI/EANN 2023 | 2023年 / 1826卷

关键词：

Machine Learning; Classification; Water Conductivity;

D O I：

10.1007/978-3-031-34204-2_9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

During the "water cycle" process, inorganic as well as organic substances are dissolved, which is completely normal. Organic substances can originate from decaying tree leaves that fall into rivers and lakes, from sewage from living organisms that live in water (e.g. fish) and human waste. Inorganic substances can come from lead and copper in water pipes, from pesticides and generally from various human activities. All these elements contribute to increase of water conductivity. The higher the conductivity in water, the more dangerous it becomes for humans [4]. The purpose of this research is to evaluate and classify water conductivity levels at the "Bramianon" dam of Crete, with the development of powerful Machine Learning models capable of successfully assigning three labels "Low", "Medium", "High".

引用

页码：97 / 109

页数：13

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