Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform

Heddam, Salim; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Falah, Mayadah W.; Goliatt, Leonardo; Tan, Mou Leong; Sa’adi, Zulfaqar; Ahmadianfar, Iman; Saggi, Mandeep Kaur; Bhatia, Amandeep Singh; Samui, Pijush

doi:10.1007/s11356-022-21201-1

Cited by 15 publications

(3 citation statements)

References 76 publications

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“…On the remote sensing side, ML algorithms have been used extensively with high accuracy to predict patterns in algal blooms via satellite image classification and characterization. − However, such studies are more often used to predict saltwater HABs. Recently, long–short-term-memory (LSTM) networks, which are a variety of recurrent neural networks (RNNs), have been used for time series analysis for HAB proxies in inland water systems .…”

Section: Machine Learning Methods Applied To Habsmentioning

confidence: 99%

“…Heddam et al 96 built multiple ML models including ANNs, extreme learning machine (ELM), random forest regression (RFR), and random vector functional link (RVFL) for modeling cyanobacteria at two rivers located in the United States using only water quality variables. They found that good predictive accuracy was obtained using the RFR model, while the other models, i.e., ANN, RVFL, and ELM, failed to provide a good estimation of the cyanobacteria concentrations.…”

Section: Machine Learning Methods Applied To Habsmentioning

confidence: 99%

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Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

Marrone,

Banerjee,

Talapatra

et al. 2023

ACS EST Water

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Freshwater cyanobacterial harmful algal blooms (cyanoHABs) are a worldwide problem resulting in substantial economic losses, due to harm to drinking water supplies, commercial fishing, wildlife, property values, recreation, and tourism. Moreover, toxins produced from some cyanoHABs threaten human and animal health. Climate warming can affect the distribution of cyanoHABs, where rising temperatures facilitate more intense blooms and a greater distribution of cyanoHABs in inland freshwater. Nutrient runoff from adjacent watersheds is also a major driver of cyanoHAB formation. While some of the physicochemical factors behind cyanoHAB dynamics are known, there are still major gaps in our understanding of the conditions that trigger and sustain cyanoHABs over time. In this perspective, we suggest that sufficient data sets, as well as machine learning (ML) and artificial intelligence (AI) tools, are available to build a comprehensive model of cyanoHAB dynamics based on integrated environmental/climate, nutrient/water chemistry, and cyanoHAB microbiome and 'omics data to identify key factors contributing to HAB formation, intensity, and toxicity. By taking a holistic approach to the analysis of all available data, including the rapidly growing number of biological data sets, we can provide the foundational knowledge needed to address the increasing threat of cyanoHABs to the security of our water resources.

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Section: Machine Learning Methods Applied To Habsmentioning

confidence: 99%

Section: Machine Learning Methods Applied To Habsmentioning

confidence: 99%

Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

Marrone,

Banerjee,

Talapatra

et al. 2023

ACS EST Water

View full text Add to dashboard Cite

show abstract

“…Bachmann-Machnik et al, 2019;Chen & Chang, 2014;Naloufi et al, 2021) and cyanobacteria (e.g. Giere et al, 2020;Heddam et al, 2022;Luo et al, 2017) can improve reliability of early warning. In situ automated sampling and analysis (e.g.…”

Section: Opportunities To Improve Functional Quality Of Urban Surface...mentioning

confidence: 99%

Functional quality of urban surface water

Meulen

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The key urban water management concept is sustainable or integrated Urban Water Resources Management (UWRM). This concept promotes integration of water management and spatial planning for holistic management of all parts of the water cycle, including both natural and technical water infrastructure (Feilberg & Mark, 2016;Hering & Vairavamoorthy, 2018;Kirshen et al., 2018). The integration of water management and urban spatial planning is also advocated in the United Nations' New Urban Agenda (UN, 2017). Integrated management requires knowledge of each component of the water system (Mosleh & Negahban-Azar, 2021); surface water is one such component. To create This research is targeted at water-rich cities with relatively high water quality, such as in Europe and Northern America, because their surface water system can support a broad spectrum of water uses. The study areas for in-depth assessments in this thesis are the cities of Amsterdam in The Netherlands (Europe), Ghent in Belgium (Europe) and Toronto in Canada (Northern America). Amsterdam serves as study area in each phase of the research, while Ghent and Toronto are used as study area for specific research activities.The assessment of the current use of and future demand for urban surface water use functions is conducted for Amsterdam and Toronto. These cities are selected because they represent cities on different continents, with different climates and geographic characteristics, including different types of surface waters (Table 1.1). This variation broadens the relevance of this study. Amsterdam and Toronto are medium sized cities with respectively 854,047 and 2.96 million inhabitants. This is a relevant size class for studying urban surface water use and demand because most city dwellers in Europe and Northern America live in cities with a population of respectively 500,00 to 5 million inhabitants and 1 to 10 million inhabitants; this is expected to continue through 2030 (UN, 2019). In both cities, urban redevelopment leads to land use change and densification. Together with local water quality improvements, especially reductions of nutrient concentrations and faecal pollution (

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Application of a Fuzzy Based Machine Learning Approach to the Detection of Harmful Algae in Water Monitoring

Penzel

Rudolph

Borsdorf

et al. 2023

Lecture Notes in Networks and Systems

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Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform

Cited by 15 publications

References 76 publications

Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

Functional quality of urban surface water

Application of a Fuzzy Based Machine Learning Approach to the Detection of Harmful Algae in Water Monitoring

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