A cumulative-risk assessment method based on an artificial neural network model for the water environment

Shi, Er-Wei; Shang, Yanchen; Li, Yafeng; Zhang, Miao

doi:10.1007/s11356-021-12540-6

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…Randomly generate 10,000 similar services for each node, and randomly generate QoS attribute values for each service, and convert them into values within a certain range according to the standardization of the attribute values, which is convenient for calculation. In order to improve the accuracy, each experiment was run 500 times, and the most suitable service was selected from 10,000 services each time, compare this method with Improved Genetic Algorithm (IGA) [35] and Artificial Neural Network Algorithm (ANNA) [36].…”

Section: Performance Comparison With Other Matchingmentioning

confidence: 99%

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Fan

2021

Complexity

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With the development of the mobile Internet of Things, people have higher and higher requirements for services, and how to provide high-quality services that meet the needs of users has also become a hot issue of current research. This article is also in the context of the mobile Internet of Things, researching the solution to the service matching problem. In order to provide high-quality grid services, the grid system needs the support of quality of service (QoS). Based on the existing QoS matching strategy, a service matching strategy based on dynamic grid QoS is proposed. This paper combines semantic information, considers service quality QoS and user preferences, and improves the success rate of service matching through hierarchical matching, so as to provide users with high-quality services. At the same time, the classic Max-Min algorithm in grid task scheduling is analysed, and on this basis, the impact of QoS on task scheduling is fully considered, and combined with the concept of execution cost, a grid task based on QoS constraints (MOS) is proposed. Through simulation verification and analysis, the strategy in this paper not only achieved higher matching performance, but also improved the success rate of task execution, and the effect of system load balancing was also significantly improved.

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Section: Performance Comparison With Other Matchingmentioning

confidence: 99%

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Fan

2021

Complexity

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“…Each hidden layer includes multiple neurons that receive the values of neurons from the prior layer, process the input with activation functions, and transmit the outcomes to the neurons in the next layer with specific weights. We set the number of hidden layers and neurons for each layer with a trial‐and‐error process used in similar DFNN‐based research (Jiang et al., 2013; Leung et al., 2003; Shi et al., 2021). The process includes (i) arbitrarily determining the initial setting of neural network structure (i.e., number of layers and number of neurons in each layer) and (ii) adjusting the setting to improve the performance of the neural network.…”

Section: Developing Deep Risk Assessment Modelsmentioning

confidence: 99%

Explainable deep learning powered building risk assessment model for proactive hurricane response

Gao

Wang

2022

Risk Analysis

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Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities’ preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building‐level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine‐grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model‐agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep‐learning models in integrating multi‐sourced features and accurately predicting buildings’ risks of weather extremes for property owners and households. The AI‐powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long‐term climate adaptation.

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“…At present, ANN has been applied in numerous elds, including water environmental monitoring and assessment (Aghav et al, 2011;Wu et al, 2021). Shi et al (2021) analyzed the back propagation arti cial neural network, a self-adapting algorithm, proposed to assess cumulative risks to aquatic ecosystems. Daiem et al (2018) utilized that submerged bio lter media (plastic and gravel) under the in uence of different variables such as temperature (18.00-28.50°C), ow rate (272.16-768.96 m 3 /day), and in uent COD (55.50-148.90 ppm) combined with two radial basis function neural network (a conventional and based on particle swarm optimization) to accurately predict the removal of COD from polluted water streams.…”

Section: Introductionmentioning

confidence: 99%

The effects of socioeconomic activities on water quality in Hainan Island, south China

Qiao

et al. 2023

Preprint

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Water quality is intimately related to the livelihood of the numerous people, and affects the development and operating benefits of various industries in society. This study clarifies the effects of human-driven economic activities on inland water quality in Hainan Island, and reveals relationships between water quality and tourism on the island. Based on previously monitored data, this study uses a static Bayesian network and radial basis function neural network (RBFNN) to model and predict the future water quality. From 2012 to 2015, water quality in the Nandu, Wanquan and Changhua Rivers was good (at level II, GB3838-2002). The static Bayesian network demonstrated that Gross Output Value (GOV) of agriculture, GOV of fishery, GOV of animal husbandy and chemical oxygen demand (COD) discharge will significantly affect water quality in the Nandu and Changhua Rivers. The effect of tourism on water quality in Wanquan River was significantly higher than that on the Nandu and Changhua Rivers. In the Wanquan River, the DO content fluctuated greatly in comparison to the other two rivers, and unexpectedly, increased tourism led to higher DO values. However, it remains necessary to closely monitor negative changes in water quality due to tourism, especially in Wanquan River and eastern Hainan province. The developed RBFNN showed that the changes in water quality were predicted accurately in comparison with experimental values in the present study and the water quality also is continuously improving. Overall, results suggest that current anthropogenic socioeconomic activities had a modest effect on water quality in Hainan Island. Agriculture, fishery, animal husbandy and COD discharge were relatively important factors affecting water quality, while tourism had a perceptible effect in eastern Hainan. Our findings provide a reference for the balance of water quality, people’s livelihood and economic development (tourism and port construction) in Hainan province.

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A cumulative-risk assessment method based on an artificial neural network model for the water environment

Cited by 12 publications

References 32 publications

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Mobile Internet of Things Dynamic Grid QoS Service Matching Mechanism and Simulation Analysis

Explainable deep learning powered building risk assessment model for proactive hurricane response

The effects of socioeconomic activities on water quality in Hainan Island, south China

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