Assessment and Application of Beach Quality Based on Analytic Hierarchy Process in Yangkou Beach, Qingdao

Li, Haimeng; Wang, Jingyi; Zhu, Chaoyue

doi:10.1007/s11802-023-5414-y

Cited by 6 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The difference is that the horizontal well is more sensitive to changes in cohesion. The sensitivity analysis results may be different from the results reported in previous research studies [26,27,29,31]; this may be caused by different in situ stress mechanisms and distribution characteristics of the input parameters. In the analysis of borehole fracture pressure in a reservoir of interest, multiple methods should be adopted to measure the horizontal minimum in situ stress to obtain a higher measurement accuracy of the horizontal minimum in situ stress.…”

Section: Influence Of Single Parameter On the Risk Of Borehole Instab...contrasting

confidence: 87%

“…The results showed that the borehole instability risk evaluation method established based on the reliability theory could quantitatively evaluate borehole stability. The evaluation index of borehole instability risk probability could provide a basis for selecting traditional borehole stability coefficient and evaluating borehole stability under uncertain conditions [31][32][33][34][35]. Sheng et al [36] analyzed the sensitivity of factors affecting the reliability of borehole stability to stress, rock strength, and other factors by using the method of borehole instability risk so as to help improve the accuracy of sensitive factors by taking certain measures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity

Gao,

Wang,

Shi

et al. 2023

Processes

View full text Add to dashboard Cite

In the study of borehole instability, the majority of input parameters often rely on the average values that are treated as fixed values. However, in practical engineering scenarios, these input parameters are often accompanied by a high degree of uncertainty. To address this limitation, this paper establishes a borehole stability model considering the uncertainty of input parameters, adopts the Monte Carlo method to calculate the borehole stability reliability at different drilling fluid densities, evaluates the sensitivity of borehole instability to a single parameter, and studies the safe drilling fluid density window at different borehole stability reliability values under multi-parameter uncertainties. The results show that the uncertainty of rock cohesion has a great influence on the fracture pressure of the vertical and horizontal wells. The minimum horizontal stress has the greatest influence on the fracture pressure of the vertical and horizontal wells, followed by pore pressure. In the analysis of borehole stability, the accuracy of cohesion and minimum horizontal stress parameters should be improved. In scenarios involving multiple parameter uncertainties, while the overall trend of the analysis results remains consistent with the conventional borehole stability outcomes, there is a noteworthy narrowing of the safe drilling fluid density window. This suggests that relying on conventional borehole stability analysis methods for designing the safe drilling fluid density window can considerably increase the risks of borehole instability. Uncertainty assessment is crucial to determine the uncertainties associated with the minimum required mud pressure, thereby ensuring more informed decision-making during drilling operations. To meet practical application demands, structure and boundary condition uncertainties should be implemented for a more comprehensive assessment of borehole stability.

show abstract

Section: Influence Of Single Parameter On the Risk Of Borehole Instab...contrasting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity

Gao,

Wang,

Shi

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…In the overall safety of sluice, seepage safety occupies an important position. Within the data, it can be seen that many geological disasters and accidents are related to seepage damage [4]. Many sluices have many defects in technology and equipment, which makes the problem of seepage increasingly prominent.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

Huang,

Gu,

Peng

et al. 2024

Water

View full text Add to dashboard Cite

The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and dependency relationship in the sequence and uses the multi-strategy improved Harris Hawks optimization algorithm (MHHO) to analyze its two hyperparameters: By optimizing the number of forward and backward neurons, the overfitting and long-term dependence problems of the neural network are solved, and the convergence rate is accelerated. Based on this, the MHHO-BiLSTM statistical prediction model of sluice seepage is established in this paper. To begin with, the prediction model uses water pressure, rainfall, and aging effects as input data. Afterward, the bidirectional long short-term memory neural network parameters are optimized using the multi-strategy improved Harris Hawks optimization algorithm. Then, the statistical prediction model based on the optimization algorithm proposed in this paper for sluice seepage is proposed. Finally, the seepage data of a sluice and its influencing factors are used for empirical analysis. The calculation and analysis results indicate that the optimization algorithm proposed in this paper can better search the optimal parameters of the bidirectional long short-term memory neural network compared with the original Harris Eagle optimization algorithm, optimizing the bidirectional long short-term memory neural network (HHO-BiLSTM) and the original bidirectional long short-term memory neural network (BiLSTM). Meanwhile, the bidirectional long and short-term neural network (BiLSTM) model shows higher prediction accuracy and robustness.

show abstract

“…Sipunculans, commonly named peanut worms, are a group of non-segmented coelomic animals living in different oceanic habitats, from intertidal to abyssal zones [ 1 ]. There are about 150 species in the Sipuncula all over the world, and 39 species are identified in China [ 1 , 2 ]. Due to their economic and nutritional values, some of them have become important culture species in China.…”

Section: Introductionmentioning

confidence: 99%

“…S. australe, P. arcuatum, and S. nudus are three important edible sipunculans in intertidal zones of Hainan Island [ 1 , 18 ]. Until now, the research on the bacterial community of gut contents and surrounding sediments of S. australe and P. arcuatum had not been studied.…”

Section: Introductionmentioning

confidence: 99%

Composition of Particulate Matter and Bacterial Community in Gut Contents and Surrounding Sediments of Three Sipunculan Species (Siphonosoma australe, Phascolosoma arcuatum, and Sipunculus nudus)

Liu

Gao

et al. 2023

IJMS

View full text Add to dashboard Cite

Siphonosoma australe, Phascolosoma arcuatum, and Sipunculus nudus are three important sipunculan species in tropical intertidal zones. In this study, the particle size, organic matter content, and bacterial community composition in the gut contents of three different sipunculans and their surrounding sediments were analyzed. The grain size fractions of sipunculans’ guts were significantly different from those of their surrounding sediments; particle size fractions < 500 μm were favored by the sipunculans. As for the total organic matter (TOM), higher contents of organic matter were observed in the guts than in the surrounding sediments in all three sipunculan species. The bacterial community composition of all the samples was investigated by 16S rRNA gene sequencing, in which a total of 8974 OTUs were obtained from 24 samples based on a 97% threshold. The predominant phylum identified from the gut contents of three sipunculans was Planctomycetota, while the predominant phylum in their surrounding sediments was Proteobacteria. At the genus level, the most abundant genus was Sulfurovum (average 4.36%) in the surrounding sediments, while the most abundant genus was Gplla (average 12.76%) in the gut contents. The UPGMA tree showed that the samples from the guts of three different sipunculans and their surrounding sediments were clustered separately into two groups, which showed that these three sipunculans had a different bacterial community composition with their surrounding sediments. The grain size and total organic matter (TOM) had the greatest impacts on the bacterial community composition at both the phylum and genus levels. In conclusion, the differences in particle size fractions, organic matter content, and bacterial community composition between the gut contents and surrounding sediments in these three sipunculan species might be caused by their selective ingestion.

show abstract

Assessment and Application of Beach Quality Based on Analytic Hierarchy Process in Yangkou Beach, Qingdao

Cited by 6 publications

References 21 publications

Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity

Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity

A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

Composition of Particulate Matter and Bacterial Community in Gut Contents and Surrounding Sediments of Three Sipunculan Species (Siphonosoma australe, Phascolosoma arcuatum, and Sipunculus nudus)

Contact Info

Product

Resources

About