A Study on the Occurrence Characteristics of Harmful Blue-Green Algae in Stagnant Rivers Using Machine Learning

Author:

Jung Woo Suk1,Jo Bu Geon2,Kim Young Do2ORCID

Affiliation:

1. Nakdong River Support Team, Presidential Water Commission, Changwon-si 51439, Republic of Korea

2. Department of Civil & Environmental Engineering, Myongji University, Yongin 17058, Republic of Korea

Abstract

Several changes have occurred in the river environment of Nakdong river due to the construction of multifunctional weirs as part of the Four Major Rivers Project. This river currently exhibits characteristics that are similar to those of a stagnant water area in which the river depth increases and the flow velocity decreases. Consequently, blue-green algae are frequently observed. Toxic substances secreted by blue-green algae are harmful to aquatic ecosystems and the human body; therefore, ensuring the stability of the water quality of Nakdong river is of utmost importance. Various factors are associated with the occurrence of blue-green algae. Therefore, the causal relationship between these causative factors must be identified. In this study, we investigated factors influencing algal growth, such as water quality, hydraulics, and weather, and algal occurrence patterns by site were analyzed. Recent studies have used data mining and machine-learning techniques in algal management to quantitatively identify the characteristics of blue-green algae. In machine learning, the prediction results differ depending on the selection of parameters, which are an important aspect in the management of blue-green algae with complex causes. In this study, we quantitatively analyzed the conditions for the occurrence of cyanobacteria according to the influencing factors using decision trees and random forests, which are machine-learning techniques, along with an analysis of the major complex factors influencing the occurrence of blue-green algae in the Nakdong river weirs. Considering the water quality and hydraulic factors, we analyzed the characteristics of algal generation in each weir at different hydraulic volume times. In addition, we investigated the possibility of improving the accuracy of cyanobacterial prediction according to the learning factors. Through these analyses, we attempted to study the characteristics of blue-green algae in stagnant rivers.

Funder

National Research Foundation of Korea

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference24 articles.

1. Effect of abrupt topographical characteristic change on water quality in a river;Jung;KSCE J. Civ. Eng.,2019

2. Global occurrence of harmful cyanobacterial blooms and N, P-limitation strategy for bloom control;Ahn;Korean J. Environ. Biol.,2015

3. Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset;Harris;Lake Reserv. Manag.,2017

4. Succession of cyanobacterial species and taxonomical characteristics of Dolichospermum spp.(nostocales, cyanophyceae) in the weir regions of the Nakdong River;Ryu;J. Korean Soc. Water Environ.,2018

5. The analysis of phytoplankton community structure in the middle-lower part of the Nakdong River;Son;J. Korean Soc. Environ. Eng.,2013

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3