Grey model for prediction of pore pressure change

Mohammed, Mebruk; Watanabe, Kunio; Takeuchi, Shinji

doi:10.1007/s12665-009-0287-y

Cited by 15 publications

(5 citation statements)

References 15 publications

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“…For a steady-state simulation, these nodal equations are algebraic equations. TAGSAC has proofed to be applicable in a number of researches done all over the globe [17].…”

Section: Groundwater Modelingmentioning

confidence: 99%

Modeling for Inter-Basin Groundwater Transfer Identification: The Case of Upper Rift Valley Lakes and Awash River Basins of Ethiopia

Mohammed¹,

Ayalew²

2016

JWARP

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Groundwater movement beneath watershed divide is one component of the hydrological cycle that is typically ignored due to difficulty in analysis. Numerical groundwater models, like TAGSAC, have been used extensively for predicting aquifer responses to external stresses. In this paper TAGSAC code was developed to identify the inter-basin groundwater transfer (IBGWT) between upper Awash River basin (UARB) and upper rift valley lakes basin (URVLB) of Ethiopia. For the identification three steady state groundwater models (for UARB, URVLB and for the two combined basins) were first created and calibrated for the 926 inventoried wells. The first two models are conceptualized by considering the watershed divide between the two basins as no-flow. The third model avoids the surface water divide which justifies IBGWT. The calibration of these three models was made by changing the recharge and hydrogeologic parameters of the basins. The goodness of fit indicators (GoFIs) obtained was better for the combined model than the model that describes the URVLB. Furthermore, the hydraulic head distribution obtained from the combined model clearly indicates that there is a groundwater flow that doesn't respect the surface water divide. The most obvious effect of IBGWT observed in these two basins is that it diminishes surface water discharge from URVLB, and enhances discharge in the UARB. Moreover, the result of this study indicates potential for internal and cross contamination of the two adjacent groundwater.

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“…For a steady-state simulation, these nodal equations are algebraic equations. TAGSAC has proofed to be applicable in a number of researches done all over the globe [17].…”

Section: Groundwater Modelingmentioning

confidence: 99%

Modeling for Inter-Basin Groundwater Transfer Identification: The Case of Upper Rift Valley Lakes and Awash River Basins of Ethiopia

Mohammed¹,

Ayalew²

2016

JWARP

Self Cite

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“…Expedite the development of an industrial logistics center in Taiwan Grey Theory Grey system is used in a variety of fields. For example, Grey system is used in monitoring changes of groundwater flow caused by any construction work in fractured rock mass 6 . Grey model may be used in improving conventional image edge algorithms through which the information and problems may be generated and undetected 7 .…”

Section: Ecfa (Economic Cooperation Framework Agreement)mentioning

confidence: 99%

Impact of ECFA on Trend of Exchange Rate between NTD and RMB Based on Grey Prediction Model

Chen

2011

AMR

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Beginning from 2001 in which both Taiwan and China joined World Trade Organization (WTO), a large number of Taiwanese manufacturers have moved their plants across Taiwan Straight to China in search for cheaper labor force regardless of the political tensions between two countries. With the gradual easing of cross-strait political tension after the inauguration of pro-China president of Taiwan, the Taiwan Government starting from 2009 has been promoting the "Economic Cooperation Framework Agreement" (ECFA) through which the government claims the cross-strait economic and trade relations could be further enhanced and Taiwan could avoid being “marginalized” from the regional economic system. Additionally, as the two countries gradually lift restrictions on the investment and remove tariff barriers the cross-strait economic is bound to be more closely and the trade exchanges are set to skyrocket. However, since the bilateral trade or investment is generally through the U.S. dollar the fluctuation in currency exchange rate may serve as one of the important factors when the business makes the investment decision. Therefore, this paper applies Grey prediction method of Grey theory to the forecast of the exchange rate between NT dollar and RMB on which Taiwan enterprises could depend when formulating their investment strategies of investing more in China or Taiwan.

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“…Their study highlighted the Levenberg Marquardt (LM) algorithm as particularly efficient and rapid in modeling the dynamics of soil pore water pressure changes in response to rainfall patterns. Mohammed proposed a "grey model" that integrates the finite element method (FEM) with the ANNs [32], aiming for a more accurate forecast of PWP variations. Tayfur et al (2005) [33] conducted a comparative analysis between FEM and ANNs for flow seepage through the Jeziorsko earth-fill dam in Poland.…”

Section: Introductionmentioning

confidence: 99%

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

An,

Dias,

Carvajal

et al. 2024

Applied Sciences

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Pore water pressure (PWP) response is significant for evaluating the earth dams’ stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models’ robustness and the hyperparameter’s influence on the performance of the prediction model.

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Grey model for prediction of pore pressure change

Cited by 15 publications

References 15 publications

Modeling for Inter-Basin Groundwater Transfer Identification: The Case of Upper Rift Valley Lakes and Awash River Basins of Ethiopia

Modeling for Inter-Basin Groundwater Transfer Identification: The Case of Upper Rift Valley Lakes and Awash River Basins of Ethiopia

Impact of ECFA on Trend of Exchange Rate between NTD and RMB Based on Grey Prediction Model

Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case

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