Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling

Brester, Christina; Ryzhikov, Ivan; Siponen, Sallamaari; Jayaprakash, Balamuralikrishna; Ikonen, Jenni; Pitkänen, Tarja; Miettinen, Ilkka T.; Torvinen, Eila; Kolehmainen, Mikko

doi:10.1016/j.scitotenv.2020.137249

Cited by 9 publications

(5 citation statements)

References 63 publications

(66 reference statements)

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“…The use of a miniature network also enabled the comparison between the bacterial communities in the water, leaving the miniature network with the water in the actual distribution network. Miniature pilot distribution networks have been applied in previous studies (Liu et al, 2013;Álvarez-Arroyo et al, 2015;Abokifa et al, 2016;Brester et al, 2020;Zhou et al, 2020). Liu et al (2013), for example, evaluated the impact of various treatment schemes on water quality during distribution using miniature networks.…”

Section: Significance Of Results From Simulative Miniature Distributi...mentioning

confidence: 99%

Seawater desalination based drinking water: Microbial characterization during distribution with and without residual chlorine

et al. 2022

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Section: Significance Of Results From Simulative Miniature Distributi...mentioning

confidence: 99%

Seawater desalination based drinking water: Microbial characterization during distribution with and without residual chlorine

et al. 2022

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“…Total nucleic acids were extracted from DEUF concentrates on membrane filters as described previously in reference 104 using Chemagic DNA plant kit (Perkin Elmer, Waltham, MA, USA). Total RNA was further purified using Ambion Turbo DNA-free DNase kit (Life Technologies, Carlsbad, CA, USA).…”

Section: Methodsmentioning

confidence: 99%

Surface Water Intrusion, Land Use Impacts, and Bacterial Community Composition in Shallow Groundwater Wells Supplying Potable Water in Sparsely Populated Areas of a Boreal Region

et al. 2021

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Standard physicochemical water quality analyses and microbial indicator analyses leave much of the (largely uncultured) complexity of groundwater microbial communities unexplored. This study combined these standard methods with additional analyses of stable water isotopes, bacterial community data, and environmental data about the surrounding areas to investigate the associations between physicochemical and microbial properties of 28 shallow groundwater wells in Finland.

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“…Validation results showed that the stacked model was able to accurately predict the WQI even with limited data [157]. Brester et al utilized temperature, pH, and the concentrations of copper and iron ions collected from the pilot drinking water distribution system as features and developed an RF-based model to predict the abundance of bacterial populations in real time [158]. Liu et al developed a drinking water quality model that utilized characteristics such as pH, DO, conductivity, turbidity, and chemical oxygen demand to predict water quality [159].…”

Section: Water Quality Predictionmentioning

confidence: 99%

Research on Water Resource Modeling Based on Machine Learning Technologies

Liu,

Zhou,

Yang

et al. 2024

Water

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Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling.

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Potential and limitations of a pilot-scale drinking water distribution system for bacterial community predictive modelling

Cited by 9 publications

References 63 publications

Seawater desalination based drinking water: Microbial characterization during distribution with and without residual chlorine

Seawater desalination based drinking water: Microbial characterization during distribution with and without residual chlorine

Surface Water Intrusion, Land Use Impacts, and Bacterial Community Composition in Shallow Groundwater Wells Supplying Potable Water in Sparsely Populated Areas of a Boreal Region

Research on Water Resource Modeling Based on Machine Learning Technologies

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