A critical review of soil moisture measurement

This paper examines and assesses predictive methods for the saturated hydraulic conductivity of soils. The soil definition is that of engineering. It is not that of soil science and agriculture, which corresponds to “top soil” in engineering. Most predictive methods were calibrated using laboratory permeability tests performed on either disturbed or intact specimens for which the test conditions were either measured or supposed to be known. The quality of predictive equations depends highly on the test quality. Without examining all the quality issues, the paper explains the 14 most important mistakes for tests in rigid-wall or flexible-wall permeameters. Then, it briefly presents 45 predictive methods, and in detail, those with some potential, such as the Kozeny-Carman equation. Afterwards, the data of hundreds of excellent quality tests, with none of the 14 mistakes, are used to assess the predictive methods with a potential. The relative performance of those methods is evaluated and presented in graphs. Three methods are found to work fairly well for non-plastic soils, two for plastic soils without fissures, and one for compacted plastic soils used for liners and covers. The paper discusses the effects of temperature and intrinsic anisotropy within the specimen, but not larger scale anisotropy within aquifers and aquitards.

Predicting the saturated hydraulic conductivity of soils: a review

Accurate prediction of spatial distribution of soil organic matter (SOM) at different scales is important for various applications related to land use and environmental problems. This study proposed a radial basis function neural networks model (RBFNN), combined with principal component analysis (PCA), to predict the spatial distribution of SOM content across China. To assess its feasibility, 6241 soil samples collected during the second national soil survey period were used. To predict the SOM at such scale, the entire study area was firstly divided into 22 different soil-landscape units according to soil types and vegetation types; then 11 quantitative environmental factors derived from climate, topography, and vegetation were converted into principal components (PC) and the first five PCs which explain 92.97% of the total data variance were selected as predictors for the purpose of eliminating the mulicollinearity of these actual variables and reducing the number of predictors; finally, a specific artificial neural network model was trained for each soil-landscape unit to capture the relationships between SOM and PCs and then used to predict the distribution of SOM content within the corresponding soil-landscape unit. The performance of this approach was evaluated by several validation indices and compared with multiple linear regression (MLR) and regression kriging (RK). The results have shown that RBFNN performs much better than both MLR and RK with much higher ratio of performance to deviation (RPD) and lower prediction errors (mean absolute error (MAE), mean relative error (MRE) and root mean squared error (RMSE)). The RPD obtained by RBFNN was 1.94, which resulted in relative improvement of 29.33% compared with RK and MLR. The three prediction errors of RBFNN were smaller than that of MLR and RK by 3.10 g.kg(-1), 17.25%, 6.25 g.kg(-1), and by 234 g.kg(-1), 5.93%, 6.24 g.kg(-1) respectively. Also, RBFNN presented a more realistic spatial pattern of SOM than RK and MLR. The good performance of this method can be attributed to the division of the study area and the capability of RBFNN to capture the nonlinear relationships between SOM and environmental factors within different soil-landscape units. The result suggests that the proposed method can play a vital role in improving prediction accuracy of SOM within a large area. (C) 2012 Elsevier B.V. All rights reserved.

Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach

The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions

Tillage aims to prepare the soil with the adequate treatment to create the ideal and most favorable conditions for cultivation. To evaluate the effect of tillage systems on soil environment, it is mandatory to measure the modifications in physical, chemical and biological properties. In recent decades, artificial intelligence systems were used for developing predictive models to simplify, estimate and predict many farming processes. They are also employed to optimize performance and control risks. These systems have become true virtual helpers, and more so when integrated with predictive analytics. In the present study, the effects of tillage systems on soil properties and crop production and the predictive capabilities of multiple linear regressions (MLR) and artificial neural networks (ANN) are evaluated to estimate organic potato crop yield including soil microbial biomass (MB), soil resistance to penetration, soil organic matter (OM) and tillage system. Potato yield was found to be significantly impacted by tillage and soil properties. The results showed that MLR model estimated crop yield more accuracy than ANN model. Correlation coefficient and root mean squared (RMSE) were 0.97 and 0.077 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between potato yield, tillage and soil properties.

Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR).

Artificial neural network models for predicting soil thermal resistivity

Characteristics of fine-grained soils primarily depend on their specific-surface area and hence, reliable determination of this parameter is essential. In this context, researchers have employed quite sophisticated instruments (viz., a BET surface area analyzer, the mercury intrusion porosimetry, internal reflectance spectroscopy, X-Ray diffraction and gas pycnometer etc.) and methodologies (viz., sorption of Methylene Blue dye, Ethylene Glycol Monoethyl Ether and p-Nitrophenol) to determine specific-surface area of these soils. However, most of these methodologies are found to be quite tedious, cost and time intensive. Apart from this, the results obtained are contentious due to the inherent limitations associated with either the instruments employed or the basic assumptions made for computing the specific-surface area of the soil. Hence, it becomes mandatory to evaluate the efficiency of these methodologies for determining specific-surface area of fine-grained soils. With this in view, different types of soils were considered in this study and their specific-surface area was determined, by following different methodologies, and the results were evaluated critically. In addition, attempts were made to develop relationships between the basic properties of fine-grained soils (viz., liquid limit, cation-exchange capacity, activity, and free swell index) and the specific-surface area. These relationships will be of immense help to the practicing engineers and research fraternity.

Comparison of Methods for Determining Specific-surface Area of Fine-grained Soils

Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity

Tensile strength of fine-grained soils plays a significant role in assessing their cracking characteristics, which govern their suitabil- ity as a construction material for landfill liners and covers, earthen dams, embankments, and pavements. As such, determination of this property of these soils becomes essential. In this context, various experimental techniques that have been developed by earlier researchers to determine tensile strength of fine-grained soils are worth appreciating. Based on the experimental results obtained from these studies, several empirical relationships have been proposed. However, these relationships relate tensile strength of the soil with a single parameter (i.e., suction, plasticity index, liquid limit, CEC, clay content, or water content).This necessitates: (a) critical evaluation of such relationships, and (b) development of a generalized relationship that employs multiple soil properties. With this in view, investigations were carried out on some fine-grained soils and their tensile strength was determined by conducting undrained triaxial tests and suction measurements. Details of the methodologies adopted are presented in this paper and efforts have been made to achieve the two objectives mentioned above.

A Critical Review of the Methodologies Employed for Determination of Tensile Strength of Fine-Grained Soils

The fate and transport of reactive contaminant(s) in geomaterials (i.e., soils and rocks) and efficacy of the immobilization–remediation methodology of the contaminated geomaterials is governed mainly by the sorption and desorption characteristics of these geomaterials and immobilizing agents. As such, establishment of these characteristics becomes mandatory. To achieve this, batch tests are usually conducted and a distribution coefficient is obtained from Freundlich, linear, and Langmuir isotherms. However, the relative efficiency of these isotherms needs to be ascertained for selection of the appropriate distribution coefficient for a contaminant geomaterial – immobilizing agent system. With this in mind, conventional batch tests were conducted on various geomaterials and immobilizing agents, to establish their sorption and desorption characteristics. However, it should be noted that batch tests do not represent the real-life contaminant geomaterial – immobilizing agent interaction, and are quite cumber...

B. Hanumantha Rao

Papers

Artificial neural network models for predicting soil thermal resistivity

Comparison of Methods for Determining Specific-surface Area of Fine-grained Soils

Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity

A Critical Review of the Methodologies Employed for Determination of Tensile Strength of Fine-Grained Soils

Determination of distribution coefficient of geomaterials and immobilizing agents.