A Novel Eutrophication Assessment Models for Aquaculture Water Area via Artificial Neural Networks

Yang, Deyong; Chen, Fudi; Zhou, Yibing

doi:10.1166/jctn.2015.4198

Cited by 6 publications

(3 citation statements)

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“…Different scholars have proposed many eutrophication evaluation models [ 50 , 51 , 52 , 53 ]. Yang et al used a series of artistic neural networks to develop an eutrophication assessment model for aquaculture water areas [ 54 ]. Wu et al established a hybrid model combining water quality indicators and ecological response indicators to assess eutrophication, and then applied it to assess the status of eutrophication from 2007 to 2008 in the southwest Bohai Sea [ 55 ].…”

Section: Methodsmentioning

confidence: 99%

Contrasting Eutrophication Risks and Countermeasures in Different Water Bodies: Assessments to Support Targeted Watershed Management

Chu

Zhang

et al. 2017

IJERPH

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Eutrophication is a major problem in China. To combat this issue, the country needs to establish water quality targets, monitoring systems, and intelligent watershed management. This study explores a new watershed management method. Water quality is first assessed using a single factor index method. Then, changes in total nitrogen/total phosphorus (TN/TP) are analyzed to determine the limiting factor. Next, the study compares the eutrophication status of two water function districts, using a comprehensive nutritional state index method and geographic information system (GIS) visualization. Finally, nutrient sources are qualitatively analyzed. Two functional water areas in Tianjin, China were selected and analyzed: Qilihai National Wetland Nature Reserve and Yuqiao Reservoir. The reservoir is a drinking water source. Results indicate that total nitrogen (TN) and total phosphorus (TP) pollution are the main factors driving eutrophication in the Qilihai Wetland and Yuqiao Reservoir. Phosphorus was the limiting factor in the Yuqiao Reservoir; nitrogen was the limiting factor in the Qilihai Wetland. Pollution in Qilihai Wetland is more serious than in Yuqiao Reservoir. The study found that external sources are the main source of pollution. These two functional water areas are vital for Tianjin; as such, the study proposes targeted management measures.

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Section: Methodsmentioning

confidence: 99%

Contrasting Eutrophication Risks and Countermeasures in Different Water Bodies: Assessments to Support Targeted Watershed Management

Chu

Zhang

et al. 2017

IJERPH

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“…To this day, numerous researchers have addressed this crucial issue: in ecological and environmental domains, there are many models, such as the species diversity index (Spatharis & Tsirtsis, 2013), phytoplankton trophic index (Phillips et al, 2013), the comprehensive nutrition state index (Xu et al, 2012), integrated methodology (Wu et al, 2013), and the eutrophication index (Fertig et al, 2014), etc. Research in informatics also approaches the ecosystems' monitoring, such as genetic algorithm (Song et al, 2012), neural network (Melesse et al, 2008; Yang et al, 2015), fuzzy set theory (Giusti et al, 2011), rough set theory (Yan et al, 2016) and support vector machine (SVM) (Huo et al, 2014) while remote sensing control is now widely used for monitoring blooms such as blue‐green algae (Matthews et al, 2015; Zhou et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Variables’ classification via equivalence relations for the trophic state of a Mediterranean ecosystem

Ellina

Papaschinopoylos

Papadopoulos

2021

Water Environment Research

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The trophic state of an aquatic body is influenced by many biotic and abiotic factors. When lots of parameters affect a phenomenon, such as eutrophication, it is difficult to distinguish which are the ones that affect the ecosystem the most. In this paper, we propose an alternative way for data analysis, in order to avoid complex systems with many variables. For the examined Mediterranean shallow lake, the studied parameters are water temperature (°C), ammonia (NH4‐N) (mg/L), dissolved oxygen (mg/L), turbidity (NTU), pH, conductivity (mS/cm), and chlorophyll‐a (μg/L). We formed groups with the variables above based on fuzzy equivalence relations and from each group we chose the parameter influence the studied phenomenon the most. Numerical results of fuzzy linear regression showed strong agreement with the proposed method above and pH, NH4‐N, and dissolved oxygen are the variables influence this ecosystem more than the others. Practitioner points When having many parameters in a studied ecosystem, we propose a way we can distinguish the most representative ones, the parameters that influence more the phenomenon we study each time. Formation of groups in variables can be applied to many case studies in order to have a clear idea of our data and the relevance of each of them in our dependent one. Fuzzy linear regression can be used in order to check the final results and ensure that the equivalence relations are a such a good method used in the data analysis while the researchers save time in long procedures of analyzing parameters not very close involved to the phenomenon investigated every time.

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“…Drip irrigation is a typical application of micro-irrigation technology and one of the common water-saving irrigation systems [2]. Drip irrigation systems deliver water to the entire field with almost no terrain impact [3,4], and reduce water loss caused by evaporation and leakage. In addition, the impact of drip irrigation on soil structure is minimal while eliminating surface runoff.…”

Section: Introductionmentioning

confidence: 99%

Water Uptake Patterns of Alfalfa under Winter Irrigation in Cold and Arid Grassland

Niu

et al. 2020

Water

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Crop reduction caused by cryogenesis and drought is a serious and global problem. The environmental stress caused by low temperature and drought during the overwintering stage of forage is the key factor leading to this low yield. In cold and arid grassland, winter irrigation can effectively alleviate the stress of alfalfa during overwintering, improve the survival rate of alfalfa, and significantly increase the yield. However, the water uptake patterns of alfalfa under winter irrigation are not clear, which are important to explore the mechanism of alleviating environmental stress by winter irrigation. In this research, the stable isotope compositions of all probable water sources and alfalfa xylem water were measured after winter irrigation. A graphical method was applied to identify the main soil layers with water uptake by the alfalfa roots. The contribution rate of available water sources to alfalfa xylem water was quantified by the MixSIAR (Bayesian isotope analysis mixing model in R) model. The results indicated that alfalfa absorbed soil water when the soil water content was high enough in the root layer when under high water volume freezing irrigation (irrigation in early winter when soil is freezing) but not under low and medium water volume freezing irrigation. Alfalfa gradually began to absorb soil water on the third day after thawing irrigation (irrigation in late winter when the soil is thawing) and showed different water uptake characteristics under low, medium, and high water volume. Thawing irrigation also accelerated the regeneration of alfalfa.

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A Novel Eutrophication Assessment Models for Aquaculture Water Area via Artificial Neural Networks

Cited by 6 publications

References 0 publications

Contrasting Eutrophication Risks and Countermeasures in Different Water Bodies: Assessments to Support Targeted Watershed Management

Contrasting Eutrophication Risks and Countermeasures in Different Water Bodies: Assessments to Support Targeted Watershed Management

Variables’ classification via equivalence relations for the trophic state of a Mediterranean ecosystem

Water Uptake Patterns of Alfalfa under Winter Irrigation in Cold and Arid Grassland

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