Evaluation of algal species distributions and prediction of cyanophyte cell counts using statistical techniques

Hwang, Seong-Yun; Choi, Byung-Woong; Park, Jong-Hwan; Shin, Dong-Seok; Lee, Won-Seok; Chung, Hyeon-Su; Son, Mi-Sun; Ha, Don-Woo; Lee, Kyung-Lak; Jung, Kang-Young

doi:10.1007/s11356-023-30077-8

Cited by 1 publication

(1 citation statement)

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“…For example, Shin et al (2020) reported the development of chlorophyll-a concentration prediction models for six locations (Dasa, Waegwan, Chilgok, Dogae, Sinam, Hoesang, and Andong) in the Nakdong River basin using six machine-learning algorithms; however, they noted that an appropriate probability distribution for chlorophyll-a was not considered. Conversely, Hwang et al (2023) examined seven statistical models for predicting the cell counts of cyanophytes in Juam Lake and Tamjin Lake, major water source sites within the Yeongsan and Seomjin River Basins. The study was based on the Poisson or negative binomial distribution, typically used to explain the variability of count variables.…”

Section: Introductionmentioning

confidence: 99%

Evaluation method for probability distribution and prediction models for chlorophyll-a based on statistical models and artificial intelligence-based algorithms

Hwang,

Jung,

Park

et al. 2024

Preprint

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In South Korea, sites that are not under the algae alert system yet frequently experience algal blooms are managed as designated algal observation sites. Chlorophyll-a is one of the key water quality parameters monitored at these sites. To investigate trends in chlorophyll-a, this study used water quality monitoring data from two representative algal observation sites in the Yeongsan River basin from January 2016 to December 2002. Based on the data, an exploratory data analysis was conducted to examine the distributional characteristics of each variable, after which an appropriate probability distribution was inferred to explain fluctuations in chlorophyll-a. Building on these data, three statistical models and four artificial intelligence-based algorithms for predicting chlorophyll-a were developed, and their levels of predictive performance were quantitatively compared. The more precise methods proposed in this study for predicting chlorophyll-a levels are expected to significantly aid in water quality management at various monitoring sites.

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Section: Introductionmentioning

confidence: 99%