Optimization of urban heat effect mitigation based on multi-type ant colony algorithm

Zhang, Yaping; Chen, Xu; Lv, Danjv; Zhang, Yan

doi:10.1016/j.asoc.2021.107758

Cited by 18 publications

(4 citation statements)

References 29 publications

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“…Based on the research objectives of this paper and existing studies ( Lu et al., 2021 ; Zhang et al., 2021 ; Chen and Zhang, 2023 ), we selected representative influencing factors of climate, phenology and vegetation growth conditions, respectively. The three meteorological variables were precipitation (PREC), temperature (TEMP) and solar radiation (SRAD).…”

Section: Methodsmentioning

confidence: 99%

The impact of vegetation phenology changes on the relationship between climate and net primary productivity in Yunnan, China, under global warming

Chen,

Zhang

2023

Front. Plant Sci.

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Climate influences net primary productivity (NPP) either directly or indirectly via phenology. Therefore, clarifying the indirect effects of climate on NPP through phenology is of utmost importance. However, the underlying mechanisms by which phenology indirectly affects NPP are unknown and poorly studied. Based on different structural equation models, this study analyzed the influence of phenology on the relationship between climate and NPP, and the results were as follows: (1) Temperature and solar radiation directly affect the end and beginning of the growing season, respectively, while precipitation indirectly affects the beginning of the growing season. (2) Spring phenology mainly affects the relationship between subsequent precipitation and net primary productivity, while autumn phenology mainly affects the relationship between temperature and net primary productivity. (3) Solar radiation is the most important direct influence factor on phenology and NPP, and the relationship between it and NPP is hardly disturbed by vegetation phenology. This research holds significant scientific and applied values in enhancing our understanding of the effects of global warming, forecasting ecosystem responses in the future, and formulating adaptation strategies.

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Section: Methodsmentioning

confidence: 99%

The impact of vegetation phenology changes on the relationship between climate and net primary productivity in Yunnan, China, under global warming

Chen,

Zhang

2023

Front. Plant Sci.

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“…At the same time, the computation of Surface Temperature (ST) for the green data center is primarily connected to a, λ, ε, and b factors, and the emission of the data center's surface is computed directly using Eq. ( 19) which conducted to calculate ST [49].…”

Section: Asa Benchmarkmentioning

confidence: 99%

Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach

2024

IJIES

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Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the applying sigmoid fish swarm optimization (SiFSO) for early compromised device detection and subsequently alerting other network nodes. Additionally, our data center implements an innovative ant skyscape architecture (ASA) cooling mechanism, departing from traditional, unsustainable cooling strategies that harm the environment. To validate the effectiveness of these approaches, extensive simulations were conducted. The evaluations primarily revolved around the fish colony's ability to detect compromised devices, focusing on source tracing, realistic modelling, and an impressive 98% detection accuracy rate under ASA cooling solution with 0.16 ºC within 1,300 second. Compromised devices pose a substantial risk to green data centers, as attackers could manipulate and disrupt network equipment. Therefore, incorporating cyber enhancements into the green data center concept is imperative to foster more adaptable and efficient smart networks.

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“…As a typical heuristic swarm intelligence algorithm, ACO, proposed by Marco Dorigo in 1992, is to find the optimal planning path by probabilistic calculation. ACO is a positive feedback mechanism, where ant's choice of path is related to the pheromone concentration, and eventually the ant will choose the path with high pheromone concentration to reach the optimal solution under the regulation mechanism [54].…”

Section: B Ant Colony Optimization Strategymentioning

confidence: 99%

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

Xiong¹,

Zhou²,

Wang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslide susceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for post-earthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow convergence, and insufficient hyperparametric optimization. In response to these problems, this study proposes an ensemble model based on ant colony optimization strategy and deep belief network (ACO-DBN). In ACO-DBN, DL optimization strategies were added to DBN and their combined parameters were optimized with ACO. Next, Pearson's correlation coefficient and random forest importance ranking methods were utilized to optimize landslide causative factors. Then, the Jiuzhaigou earthquake region was selected as an example to explore the applicability of this model. Besides, we conducted the Wilcoxon signed rank test in order to verify that the differences were statistically significant. In a comprehensive comparative all indexes and landslide density, the model proposed in this paper shows good rationality, scientificity, and interpretability. The newly occurred landslide site further demonstrates that heuristically optimized DL could make scientific and accurate evaluation of landslide susceptibility.Index Terms-ant colony optimization (ACO) algorithm, deep belief network (DBN), Jiuzhaigou earthquake, landslide susceptibility mapping (LSM). I. INTRODUCTIONs one of the most common geological hazards worldwide, landslides have caused huge human casualties and economic losses every year. Especially in China, landslides are the most widespread and costly geological disasters [1]. According to the statistics yearbook of the Geological Disaster Guidance Center of the Ministry of Natural Resources, in 2020, there were 7840 geological disasters in China, of which 4810 Manuscript received xx xx, xx.

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Optimization of urban heat effect mitigation based on multi-type ant colony algorithm

Cited by 18 publications

References 29 publications

The impact of vegetation phenology changes on the relationship between climate and net primary productivity in Yunnan, China, under global warming

The impact of vegetation phenology changes on the relationship between climate and net primary productivity in Yunnan, China, under global warming

Eco-friendly and Secure Data Center to Detection Compromised Devices Utilizing Swarm Approach

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

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