Study of water quality response to water transfer patterns in the receiving basin and surrogate model

Liu, Lei; You, Xue-yi

doi:10.1007/s11356-021-17469-4

Cited by 7 publications

(1 citation statement)

References 56 publications

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“…Surrogate modeling addresses this challenge by creating data-driven models that approximate the behavior of physical models and predict the outputs of these complex systems with much less computational effort. This makes them extremely valuable in scenarios where rapid decision-making is crucial, such as emergency response planning, environmental impact assessments, or policy development [11,38,60,102,132,179,180,184]. Surrogate models are typically developed using advanced machine learning tools [11,60,144,158,179,184] (e.g., Koopman operator [15]) or statistical methods [43], and are trained on simulated data generated by complex physical models.…”

Section: Surrogate Modeling Solving Pdesmentioning

confidence: 99%

Knowledge-Guided Machine Learning

Karpatne¹,

Kannan²,

Kumar³

2022

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This paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based models. It also provides an introduction to the current state of research in the emerging field of scientific knowledge-guided machine learning (KGML) that aims to use both scientific knowledge and data in ML frameworks to achieve better generalizability, scientific consistency, and explainability of results. We discuss different facets of KGML research in terms of the type of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML. We also discuss some of the common categories of use cases in environmental sciences where KGML methods are being developed, using illustrative examples in each category.

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Section: Surrogate Modeling Solving Pdesmentioning

confidence: 99%

Knowledge-Guided Machine Learning

Karpatne¹,

Kannan²,

Kumar³

2022

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The impact of household wastewater on the frailty state of the elderly in China: based on a long-term cohort study in China

Wang

Zhou

et al. 2022

Environ Sci Pollut Res

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Impacts of environmental flow regulation on survival of aquatic organisms: a case study of Cyprinus Carpio in Baiyangdian, China

Ma,

You

2023

Environ Sci Pollut Res

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Decreasing water volume and increasing pollutants in wetlands pose challenges to aquatic life.Environmental ow regulation is a widely used to improve the living environment of aquatic organisms, but it's effectiveness needs to be evaluated. In this study, the hydrodynamic and water quality models are used to simulate the elds of ow, temperature and pollutants and the Ecological Niche Modelling at the MetaLand EcologyLab (ENMTML) is utilized to evaluate the area of suitable habitats of aquatic organisms in the cases of environmental ow regulation and no environmental ow regulation conditions. The typical wetland of Baiyangdian in northern China is selected as the studied area and the important economic sh Cyprinus carpio is select as the indicator aquatic species to show the effectiveness of environmental ow regulation. The study period is from December 1, 2017 to July 1, 2018. The results indicate that for Cyprinus carpio in Baiyangdian Wetland, the variables of water depth, dissolved oxygen (DO), ammonia nitrogen (NH4+-N) and Chlorophyll a (Chla) are the major environmental factors which determines the variability of suitable habitat extent. The environmental ow regulation amount for the Baiyangdian Wetland is 2.6 10 8 m 3 , which produces a suitable breeding habitat area of 135.532 km 2 at the end of the water supply period. Comparing with no environmental ow regulation case, the suitable habitats areas is enlarged by 56.29 and 31.83 km 2 of the highly and moderately suitable habitats for Cyprinus carpio, respectively. The outcomes of this study provide not only a basic reference for wetland management, but also a scienti c perspective understanding of the environmental ow regulation on aquatic organisms. The proposed method combining MIKE 21 model with the ENMTML package is shown to have the potential to evaluate the effectiveness of environmental ow regulation in wetlands.

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Study of water quality response to water transfer patterns in the receiving basin and surrogate model

Cited by 7 publications

References 56 publications

Knowledge-Guided Machine Learning

Knowledge-Guided Machine Learning

The impact of household wastewater on the frailty state of the elderly in China: based on a long-term cohort study in China

Impacts of environmental flow regulation on survival of aquatic organisms: a case study of Cyprinus Carpio in Baiyangdian, China

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