scholarly journals A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Binh Thai Pham ◽  
Hai-Bang Ly ◽  
Nadhir Al-Ansari ◽  
Lanh Si Ho
Keyword(s):  
Gaussian Process ◽  
Soft Computing ◽  
Permeability Coefficient ◽  
Computing Methods ◽  
Machine Learning Techniques ◽  
Soil Parameters ◽  
Soil Permeability ◽  
Difficult Time ◽  
Accuracy Of Prediction ◽  
Gp Model

The permeability coefficient (k) of soil is one of the most important parameters affecting soil characteristics such as shear strength or settlement. Thus, determining soil permeability coefficient is very crucial; however, a field test for determining this parameter is difficult, time-consuming, and expensive. In this study, soft computing methods, namely, M5P and Gaussian process (GP), for estimating the permeability coefficient were constructed and compared. The results of this paper indicate that the two soft computing algorithms functioned well in predicting k. These two methods gave high accuracy of prediction capability. The determination coefficient of M5P (R2 = 0.766) was higher than that (R2 = 0.700) of GP. This implies that the M5P model is more reliable estimation than the GP model in predicting soils’ permeability coefficient (k). This proves that applying these machine learning techniques can provide an alternative for predicting basic soil parameters, including the permeability coefficient of soil.

scholarly journals Geostatistical Analysis of the Permeability Coefficient in Different Soil Textures

2018 ◽  
Vol 67 (2) ◽  
pp. 119-124 ◽  
Author(s):  
Lida Issazadeh ◽  
Mustafa Ismail Umar ◽  
Said I.A. Al-Sulaivany ◽  
Jian Hassanpour
Keyword(s):  
Spatial Variability ◽  
Permeability Coefficient ◽  
Soil Samples ◽  
Geostatistical Analysis ◽  
Soil Parameters ◽  
Kurdistan Region ◽  
Soil Permeability ◽  
Interpolation Methods ◽  
Soil Hydraulic ◽  
Irrigation Drainage

Summary Estimating soil hydraulic properties are so important for hydrological modeling, designing irrigation-drainage systems and soil transmission of soluble salts and pollutants, although measurements of such parameters have been found costly and time-consuming. Owing to a high spatial variability of soil hydraulic characteristics, a large number of soil samples are required for proper analysis. Nowadays, geostatistical methods are used to estimate soil parameters on the basis of limited data. The purpose of this research is to investigate the spatial variability of the permeability coefficient in different soil textures (26 soil samples) found in the Kurdistan region of Iraq. The parameter values obtained indicated a normal trend in particle size distribution, whereas the values of permeability coefficient showed aberrant distribution patterns. Geostatistical analysis results indicated the best fitted theoretical model was Gaussian model and the proportion of sill/(sill + nugget) was 0.17 indicated strong spatial dependency of soil permeability. Furthermore, the optimal distance for estimating the soil permeability coefficient was 109,119 meters. A comparison of the kriging and IDW interpolation methods showed that both methods can estimate soil permeability with high accuracy and less error. The prediction maps of the applied methods indicated that high soil permeability rates were recorded in the south-east of the Kurdistan region of Iraq compared to low soil permeability rates recorded in the remainder of this region. It is recommended other interpolation methods such as co-kriging and indicator or simple kriging methods could be used to simulate data in large scale areas as well.

scholarly journals Evaluation of several soft computing methods in monthly evapotranspiration modelling

2017 ◽  
Vol 25 (1) ◽  
pp. 128-138 ◽  
Author(s):  
Siavash Gavili ◽  
Hadi Sanikhani ◽  
Ozgur Kisi ◽  
Mohammad Hasan Mahmoudi
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Soft Computing Methods

scholarly journals A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1492
Author(s):  
Hanita Daud ◽  
Muhammad Naeim Mohd Aris ◽  
Khairul Arifin Mohd Noh ◽  
Sarat Chandra Dass
Keyword(s):  
Gaussian Process ◽  
Inverse Modeling ◽  
Mean Squared Error ◽  
Computational Effort ◽  
Percentage Error ◽  
Depth Prediction ◽  
Receiver System ◽  
Realistic Representation ◽  
And Performance ◽  
Gp Model

Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

Evaluation of lubricant condition and engine health based on soft computing methods

2021 ◽  
Author(s):  
Mohammad-Reza Pourramezan ◽  
Abbas Rohani ◽  
Nemat Keramat Siavash ◽  
Mohammad Zarein
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Soft Computing Methods

Analysis of the Influences of Soil Parameters on the Settlement of Storage Tank Foundation

2011 ◽  
Vol 90-93 ◽  
pp. 372-376
Author(s):  
Xu Dong Zhang ◽  
Shuai Wang ◽  
Ran Gang Yu
Keyword(s):  
Permeability Coefficient ◽  
Storage Tank ◽  
Compression Modulus ◽  
Soil Parameters ◽  
Foundation Soil ◽  
Consolidation Settlement ◽  
Water Filling ◽  
Oil Tank ◽  
Tank Bottom ◽  
Main Factor

The settlement characteristics of storage tank foundation at water-filling preloading stage and stable loading stage was analyzed based on numerical simulation. And the influences of foundation soil parameters on the settlement of storage tank foundation were studied. The research results show that Uplift appears at the place 1.3D away from the center of oil tank bottom, and the final uplift value is 10mm.The main factor which influences the consolidation speed of storage tank foundation is permeability coefficient. The consolidation settlement is determined by compression modulus and load.

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