Assessment of lake eutrophication using a novel multidimensional similarity cloud model

Yao, Jiping; Wang, Guoqiang; Xue, Baolin; Wang, Puze; Hao, Fang; Xie, Gang; Peng, Yanbo

doi:10.1016/j.jenvman.2019.109259

Cited by 61 publications

(23 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liang et al [63] specified 4 simple models with time-driven switching to identify the possibility of shifts in the nutrients-phytoplankton relationship in They identified a change in this relationship and showed that an increase in nutrients did not drive this change, while total phosphorus (TP) plays a more important role than total nitrogen in an increase in Chlorophyll a; and reducing nutrients is a better strategy than ecosystem recovery to effectively reduce the concentration of Chlorophyll a. Likewise, Yao et al [64] used a multidimensional similarity cloud model to assess the level of eutrophication on the basis of different indicators, including phosphorus concentration and chlorophyll levels. The developed method was used to evaluate the degree of eutrophication of Nansi Lake in Shandong Province, China.…”

Section: Estimation Of the Thresholds In Lake Dynamicsmentioning

confidence: 99%

Dynamics and Economics of Shallow Lakes: A Survey

Gromov

Upmann

2021

Sustainability

View full text Add to dashboard Cite

We provide an overview of the results devoted to the analysis of the dynamics and economics of shallow lakes, spanning the period from 1999 until now. A shallow lake serves as a typical representative of an ecological system subject to (possibly irreversible) regime shifts. The dynamics of a shallow lake are described by a non-linear model with multiple steady states and multiple domains of attraction and is thus suitable to model the evolution of an ecosystem featuring both resilience within a domain of stability and an abrupt regime shift outside of it. Beyond this, the shallow lake model can also be viewed as a metaphor for many other ecological problems. Due to the broad applicability of this model, there is substantial interest in the management of shallow lakes and both their optimal regulation and competitive usage.

show abstract

Section: Estimation Of the Thresholds In Lake Dynamicsmentioning

confidence: 99%

Dynamics and Economics of Shallow Lakes: A Survey

Gromov

Upmann

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Their results pointed out that chemical oxygen demand and Secchi disc are the most sensitive factors. In another work, Yao et al [20] used a multidimensional similarity cloud model (MSCM) to determine a lake's eutrophication. In this paper, the criteria are the same as the one described in [19].…”

Section: Related Workmentioning

confidence: 99%

“…The data are introduced in a reverse cloud generation to obtain cloud digital characteristics, and results are compared with the different eutrophic levels. The problem with these methods [19,20] is that they need some data about the chemical composition of the water. These analyses require the use of reagents with a high cost, and in situ analyses should be performed precluding the remote monitoring.…”

Section: Related Workmentioning

confidence: 99%

Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs

Rocher

Parra

Jiménez

et al. 2021

Sensors

View full text Add to dashboard Cite

In irrigation ponds, the excess of nutrients can cause eutrophication, a massive growth of microscopic algae. It might cause different problems in the irrigation infrastructure and should be monitored. In this paper, we present a low-cost sensor based on optical absorption in order to determine the concentration of algae in irrigation ponds. The sensor is composed of 5 LEDs with different wavelengths and light-dependent resistances as photoreceptors. Data are gathered for the calibration of the prototype, including two turbidity sources, sediment and algae, including pure samples and mixed samples. Samples were measured at a different concentration from 15 mg/L to 4000 mg/L. Multiple regression models and artificial neural networks, with a training and validation phase, are compared as two alternative methods to classify the tested samples. Our results indicate that using multiple regression models, it is possible to estimate the concentration of alga with an average absolute error of 32.0 mg/L and an average relative error of 11.0%. On the other hand, it is possible to classify up to 100% of the samples in the validation phase with the artificial neural network. Thus, a novel prototype capable of distinguishing turbidity sources and two classification methodologies, which can be adapted to different node features, are proposed for the operation of the developed prototype.

show abstract

“…Fortunately, there was a multidimensional cloud model [26]. Yao et al [28] proposed a multidimensional similarity cloud model for considering data error and improving the adaptability to multiple problems. e impact of certainty and uncertainty within different indicators was felt in the model.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Connection Cloud Model Coupled with Improved CRITIC Method for Evaluation of Eutrophic Water

Yan

Wang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The degree of eutrophication in the water environment is deepening. For the appropriate treatment of eutrophication, it is essential to evaluate it accurately. However, the evaluation of eutrophication has not been well solved because it is full of uncertainty. Herein, a multidimensional connection cloud model, combined with the improved CRITIC (Criteria Importance Through Inter-criteria Correlation) method, was put forward here to assess water eutrophication and depict the randomness, ambiguity, and interaction of evaluation factors. First, an improved CRITIC was adopted to determine indicator weight so that the correlation among different indicators and more information were depicted. Secondly, a multidimensional connection cloud was simulated to characterize fuzzy indicators and ambiguous classification boundary values according to classification criteria. Next, the connection degree was calculated relative to the evaluation standard. The eutrophication grade was specified under the “maximum connection degree” principle. At last, the effectiveness and practicality of the model proposed here were affirmed by two cases and comparisons with supplementary methods. The results suggest that the proposed model can avoid shortcomings of the original CRITIC method and cloud model, and make the assessment result more realistic.

show abstract

Assessment of lake eutrophication using a novel multidimensional similarity cloud model

Cited by 61 publications

References 28 publications

Dynamics and Economics of Shallow Lakes: A Survey

Dynamics and Economics of Shallow Lakes: A Survey

Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs

Multidimensional Connection Cloud Model Coupled with Improved CRITIC Method for Evaluation of Eutrophic Water

Contact Info

Product

Resources

About