Revealing the background of groundwater level dynamics: Contributing factors, complex modeling and engineering applications

Kostić, Srđan; Stojković, Milan; Guranov, Iva; Vasović, Nebojša

doi:10.1016/j.chaos.2019.07.007

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…GICP involves fields in transportation, communication, water conservancy, and hydropower. e construction environment of these complex large scale construction projects is full of uncertainty [70], which would increase the difficulty of engineering reconnaissance and bring a negative impact on engineering construction [71]. (xiii) Stability of policy (C13).…”

Section: Specific Analysis Of Risk Factors In Gicpmentioning

confidence: 99%

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

Tao

Yong

Yang

et al. 2022

Mathematical Problems in Engineering

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Government-invested construction project (GICP) has a great significance to social and economic development but suffered many risks due to its large scale, huge investment, and long construction period. The risks in GICP are complex so as to lead the project to failure; it is extremely urgent to take the risk management of GICP. This study establishes a risk early-warning framework to help the managers to understand the risk threat in advance, which supports them to make proper management strategies for the risk control. The whole framework can be concluded as three parts: information collection, data processing, and result prediction. Firstly, the 16 risk factors of GICP are identified. To express the hesitance of human decision and reduce the information loss in quantification, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are used to collect the experts’ linguistic term and transform them into numerical value. And then, these inputs are simplified into five factors based on principal component analysis (PCA), decreasing the impact of redundancy to risk early-warning. Meanwhile, the warning level is divided based on K-means, which avoids the subjectivity of experience decision. Further, the backpropagation neural network optimized by the genetic algorithm (GA-BP) is used to complete the simulation of risk value. The 75 groups of questionnaire data are used to train the network and the 10 groups are used as test set. The validation of the proposed framework has been verified with an average relative error in 7.2% and the average absolute error in 3.91. Finally, corresponding suggestions to prevent and control the different risks in GICP are put forward.

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Section: Specific Analysis Of Risk Factors In Gicpmentioning

confidence: 99%

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

Tao

Yong

Yang

et al. 2022

Mathematical Problems in Engineering

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“…where Z denotes the simulated water levels, t symbolizes a time step, and N signifies the final time step during the simulation period. Initially, the seasonal component is deduced by utilizing the LOWESS (Locally Weighted Scatterplot Smoothing) regression method (Kostić et al, 2019), with the aim of filtering out noise (residual part) from the observed water levels. Subsequently, the filtered water levels are represented as a timely dependent function comprising stationary signals by the usage of spectral analysis (Stojković et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Colored noise in river level oscillations as triggering factor for unstable dynamics in a landslide model with displacement delay

Kostić,

Stojković

2023

Front. Earth Sci.

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In the present paper we examine the effect of the noise in river level oscillation on the landslide dynamics. The analysis is conducted in several phases. In the first phase, we analyze the multi-annual level oscillation of the Kolubara and the Ibar river (Serbia). Based on the observed dataset, we suggest a deterministic model for the river level oscillation with the additional contribution of the noise part, which we confirm to have the properties of colored noise. In the second phase of the research, we introduce the influence of the river-level oscillation, with the included effect of colored noise in the spring-block delay model of landslide dynamics. Results of the research indicate conditions under which the effect of river noise has both stabilizing and destabilizing effects on the landslide dynamics. The effect of noise intensity D and correlation time ε is systematically analyzed in interaction with delayed interaction, spring stiffness and friction parameters. It is determined that the landslide dynamics is sensitive to the change of noise intensity and that the increase of noise intensity leads to onset of unstable landslide dynamics. On the other hand, results obtained indicate that the examined model of landslide dynamics is rather robust towards the change of correlation time ε. Interaction of this parameter and some of the friction parameters leads to stabilization of landslide dynamics, which confirms the importance of the influence of the noise color in river level oscillations on the landslide dynamics.

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“…Based on groundwater dynamic data and related factors, the quantile regression method was used to predict groundwater depth [7]. Other factors affecting groundwater depth have also been investigated [8]. e development of science and technology provides convenience for the study of groundwater.…”

Section: Introductionmentioning

confidence: 99%

A Grey Seasonal Index Model for Forecasting Groundwater Depth of Ningxia Plain

Yin

Zhang

2021

Discrete Dynamics in Nature and Society

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Forecasting the depth of groundwater in arid and semiarid areas is a great challenge because these areas are complex hydrogeological environments and the observational data are limited. To deal with this problem, the grey seasonal index model is proposed. The seasonal characteristics of time series were represented by indicators, and the grey model with fractional-order accumulation was employed to fit and forecast different periodic indicators and long-term trends, respectively. Then, the prediction results of the two were combined together to obtain the prediction results. To verify the model performance, the proposed model is applied to groundwater prediction in Yinchuan Plain. The results show that the fitting error of the proposed model is 2.08%, while for comparison, the fitting error of the grey model of data grouping and Holt–Winters model is 3.94% and 5%, respectively. In the same way, it is concluded that the fitting error of groundwater in Weining Plain by the proposed model is 2.26%. On the whole, the groundwater depth in Ningxia Plain including Yinchuan Plain and Weining Plain will increase further.

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Revealing the background of groundwater level dynamics: Contributing factors, complex modeling and engineering applications

Cited by 5 publications

References 27 publications

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

Colored noise in river level oscillations as triggering factor for unstable dynamics in a landslide model with displacement delay

A Grey Seasonal Index Model for Forecasting Groundwater Depth of Ningxia Plain

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