Modelling pile capacity using Gaussian process regression

Pal, Mahesh; Deswal, Surinder

doi:10.1016/j.compgeo.2010.07.012

Cited by 123 publications

(36 citation statements)

References 30 publications

(43 reference statements)

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“…In contrast to these two methods of classification, the GPCs approach, as mentioned, is a Cornell classification, which was developed by prior Gaussian's processing functions that were originally for regression [32]. The term Gaussian Process Classifier implements Gaussian processes (GPs) for classification purposes, and more specifically for probabilistic categorization, in which test predictions are categorized as probabilities.…”

Section: Gaussian Process Classification (Gpc)mentioning

confidence: 99%

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

Azimi

Moghaddam

Monfared

2018

J Environ Health Sci Engineer

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Background The level of groundwater resources is changing rapidly and this requires the discovery of newer groundwater resources. Drought is one of the most significant natural phenomena affecting different aspects of human life and environment. During the last decades, the application of artificial intelligent techniques has been recognized as effective approaches to forecast an annual precipitation rate. Method In this study, the association analysis of climate drought and a decline in groundwater level is addressed using Gaussian process classification (GPC) and backpropagation (BP) artificial neural network (ANN). This methodology is proposed to create a framework for decision making and reduce uncertainty in water resource management calculations, and in particular to optimize the management of groundwater drinking water sources. Results Underground water levels in 609 study plains in Iran were used to predict drought over the test period, extending from 2017 to 2021. The artificial intelligence methods were implemented in the Python programming environment to achieve an annual precipitation rate. A statistical summary of the Rasterized Cells of the zoning maps was used to validate the prediction results. Considering the relationship between water quality reductions and drought in Iranian aquifers due to the occurrence of groundwater drought periods, the results were validated by analysis of the effect of climate drought using the Standardized Precipitation Index (SPI) on the occurrence of observed droughts with the Groundwater Resources Index (GRI). The results are well-illustrated by the observation of the predicted digits in the third dimension of the Gaussian distribution. Conclusion According to the SPI indicator, the southern regions of the country, and especially the central parts of the plain, can be considered the most affected areas by the most severe future droughts. The prediction results indicate a decrease in drought severity as part of a two-year sequence involving a recurrence of drought exacerbation and relative decline, as well as a failed state after the critical condition of aquifers.

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Section: Gaussian Process Classification (Gpc)mentioning

confidence: 99%

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

Azimi

Moghaddam

Monfared

2018

J Environ Health Sci Engineer

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“…However, in [Pal, 2010], the author took a different approach and investigated the potential of a Gaussian process (GP) regression techniques to predict the load-bearing capacity of piles. The results from the study indicated improved performance by GP regression in comparison to SVM and empirical relations.…”

Section: Related Workmentioning

confidence: 99%

Applicability of GMDH-Based Abducitve Network for Predicting Pile Bearing Capacity

Auta¹

2012

Automation

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“…Compared with other kernelbased regression models, GPR not only provides the estimated value of a variable of interest, but also the variance of computation which can be interpreted as a level of confidence of the model. Though it has some attractive properties beyond ANN and SVM, only recently have a limited number of soft sensor applications of GPR been reported (Chen et al, 2014;Liu et al, 2015;Yu, 2012;Pal and Deswal, 2010;Grbić et al, 2013;Ge et al, 2011). Due to its virtue for nonlinear system modeling, in this study, we are trying for the first time to use GPR to construct a soft sensor modeling of product concentration in glutamate fermentation process.…”

Section: Introductionmentioning

confidence: 99%

“…Non-parametric probabilistic models such as a Bayesian framework for Gaussian Process Regression (GPR) have received remarkable attention in the field of machine learning. In comparison to ANN, Gaussian process is easier to understand and fulfill in practice (Pal and Deswal, 2010). GPR models are closely related to SVM as a result of the use of kernel functions.…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

Zheng¹,

Pan²

2016

American Journal of Biochemistry and Biotechnology

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Abstract:The on-line control of glutamate fermentation process is difficult, owing to the typical uncertainties of biochemical process and the lack of suitable on-line sensors for primary process variables. A prediction model based on Gaussian Process Regression (GPR) is presented to predict glutamate concentration online. First, Partial Least Squares (PLS) is applied to extract the features of the input secondary variables to reduce the number of the variables dimension and eliminate the correlation, through variables selection to reduce model complexity and improve model tracking performance. Validation was carried out in a 5 L fermentation tank for 10 batches glutamate fermentation process. Simulation results show that the proposed model outperforms the PLS and Support Vector Machine (SVM) model and the Root Mean Square Error (RMSE) are 1.59, 7.98 and 1.95, respectively. It can provide effective operation guidance for control and optimization of the glutamate fermentation process.

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Modelling pile capacity using Gaussian process regression

Cited by 123 publications

References 30 publications

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

Applicability of GMDH-Based Abducitve Network for Predicting Pile Bearing Capacity

Soft Sensor Modeling of Product Concentration in Glutamate Fermentation using Gaussian Process Regression

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