Improved gray-encoded evolution algorithm based on chaos cluster for parameter optimization of moisture movement

Yang, Xiaohua; Li, Yuqi; Wang, Kaiwen; Sun, Baolin; Ye, Yi; Li, Mei-Shui

doi:10.2298/tsci160529038y

Cited by 6 publications

(6 citation statements)

References 10 publications

(12 reference statements)

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“…This evaluation method objectively reflects the clustering situation of regional index environmental factors and the corresponding confidence degree under the joint action of multiple factors, which has certain guidance for the improvement of urban residential facilities, infrastructure construction and urban ecological environment governance. It also provides necessary scientific basis for local government to make macroscopic decisions on urban human settlement environment [15]. Urban human settlement environment is a complex system with many factors influencing each other, but it is difficult to replace each other.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the suitability of human settlement environment in shanghai city based on fuzzy cluster analysis

Xue

Yang

2020

THERM SCI

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Fuzzy clustering analysis is a mathematical method to classify objective things according to their characteristics, affinity, and similarity by establishing fuzzy equivalence relations. It can solve the ambiguity of environment classification better. With the development of cities, the effluent and waste gas discharged by industrial activities have a great impact on the living environment of cities. This paper builds up an evaluation system of urban residential environment by considering society, economy, resources and environment conditions, the results show that the human settlement suitability of Shanghai became better during 2001 to 2016.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the suitability of human settlement environment in shanghai city based on fuzzy cluster analysis

Xue

Yang

2020

THERM SCI

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“…Even though there are many methods currently, it is still difficult for the current methods to meet the requirements of real-time dynamic evaluation [15,16]. In the past, most researches focused on the evaluation of the current state of water resources carrying and gave corresponding suggestions based on the evaluation results, but few studies tracked and evaluated the state of water resources carrying after taking these recommendations.…”

Section: Introductionmentioning

confidence: 99%

Dynamic evaluation and regulation of water carrying state using a coupled iterative method

2022

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The water carrying capacity and the water carrying state are two similar concepts, and there are no clear boundaries between them, so there exists confusion during the evaluation process. Additionally, current evaluation methods cannot meet the dynamic change requirements under different control measures. This study emphasizes the difference between the two concepts and points out that the core of the water carrying capacity is the determination of thresholds. In contrast, that of the water carrying state is a state evaluation. A coupled iterative model is proposed based on the extended Fourier amplitude sensitivity test algorithm and a modified state-space method. In order to evaluate and regulate the water carrying state dynamically, the iterative calculation is introduced into the evaluation of the water carrying state. During the iterative process, the water resources in the non-overloaded area are allocated to the surrounding adjacent overloaded areas until the water resource carrying state of the original over-loaded area reaches an acceptable level, and the total amount of water resources allocation during multiple iterations is given. In this study, we take Jilin Province as the research object. We hope that the iterative coupling model of the water carrying state proposed in this paper can be widely applied in the future.

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“…Another major factor that affects the distribution and functioning of ES is climate change [12]. Based on the current climate projections, if mean annual water volume remains at the same level under climate change, the increased seasonal variations of water volume and frequency of extreme hydrological events (e.g., floods, droughts) will have substantial effects on hydrological ES [13][14][15]. Climate change already affected species distribution, range, and interaction, and was projected to become a more significant threat in the coming decades [16].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Climate Change and Urban Expansion on Hydrologic Ecosystem Services in the Milwaukee River Basin

Pan

Choi

2019

Climate

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Land use/land cover (LULC) and climate changes could affect water quantity and quality and thus hydrologic ecosystem services (ES). However, studies of these impacts on hydrologic ES are limited by the current methods and techniques. We attempted to find out how the LULC and climate changes impact hydrologic ES at different temporal scales so that decision-makers can easily understand hydrologic ES variations for guiding management plans. In this study, we analyzed the impacts of LULC and climate changes on hydrologic ES in the Milwaukee River basin, USA with a conceptual modeling framework for hydrologic ES. The model framework was applied to a series of climate and urban expansion scenarios. Two hydrologic responses (streamflow and sediment) and three hydrologic ES (water provision index (WPI), flood regulation index (FRI), and sediment regulation index (SRI)) were calculated. Major findings include: (1) Climate change has much larger impacts than LULC at the monthly scale. For example, the impacts of climate change on streamflow were −6 to 9 m3/s whereas those of LULC change were −0.4 to 0.2 m3/s. Also, WPI (ranging from 0 to 1) changed between −0.16 and 0.07 with climate change but between −0.02 and −0.001 with LULC changes. (2) Compared to changes at the annual scale, the results show much larger variabilities as monthly time-series and mean monthly numbers. These findings suggest that the climate change weighs more than the realistic LULC change in term of impacts on hydrologic ES and those impacts can be identified with results at the monthly temporal scale. This approach with the framework and scenarios can better support management planning for decision-makers with detailed results and temporal precision.

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Improved gray-encoded evolution algorithm based on chaos cluster for parameter optimization of moisture movement

Cited by 6 publications

References 10 publications

Evaluation of the suitability of human settlement environment in shanghai city based on fuzzy cluster analysis

Evaluation of the suitability of human settlement environment in shanghai city based on fuzzy cluster analysis

Dynamic evaluation and regulation of water carrying state using a coupled iterative method

Impacts of Climate Change and Urban Expansion on Hydrologic Ecosystem Services in the Milwaukee River Basin

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