Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system

Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical conductivity based on the concentration of positively charged ions in water. It is shown that the ANFIS model outperforms more traditional methods of modelling electrical conductivity based on the total solids dissolved in the water, even though ANFIS uses less information. Additionally, the fuzzy rules in the ANFIS model provide a categorization of ground water samples in a manner that is consistent with the current understanding of geophysical processes

Local spatial regression models : a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis

Regional spatial analysis combining fuzzy clustering and non-parametric correlation

In this study, regional analysis based on a limited number of data, which is an important real problem in some disciplines such as geosciences and environmental science, was considered for evaluating spatial data. A combination of fuzzy clustering and non-parametrical statistical analysis is made. In this direction, the partitioning performance of a fuzzy clustering on different types of spatial systems was examined. In this way, a regional projection approach has been constructed. The results show that the combination produces reliable results and also presents possibilities for future works

Fuzzy optimization of slab production from mechanical stone properties

This paper aims to conduct slab production optimization by a flexible tool, which is fuzzy linear programming. There is a direct relationship between slab production and mechanical stone characteristics. In this process, the goal and its tolerance cannot be specified firstly due to a lack of knowledge. Therefore, the optimal system design problem for optimal slab production under soft constraints is constructed and solved in a fuzzy environment. The results show that fuzzy linear optimization is a convenient tool for optimizing slab production

Mapping water chemical variables with spatially correlated errors

One of the most important considerations in many environmental studies is need to allow for correlations among the variables. Monitoring and analyzing relationships between chemical environmental parameters using spatial correlation based regression modelling is the main motivation of this applied study. For this purpose, some noticeable environmental parameters of data sets obtained from two lakes have been considered and the concentrations of chemical variables such as cadmium and nitrate have been appraised by a regression-based geostatistical methodology. The modelling procedure consists of two stages. In the first stage, spatial variables are analyzed via multi-linear regression and some relationships are provided. Next, by using the spatial auto-correlations of the residuals, a type of regression-based kriging procedure is applied. The capacity of the model for appraising the water chemical variables is also tested and performance comparisons with ordinary kriging are conducted. Finally, the applications showed that analyzing water chemical variables with spatially correlated errors is a convenient and applicable approach for assessing the environmental systems. © 2012 Springer Science+Business Media, LLC

A linguistic model for evaluating cement strength

This paper presents a soft methodology for predicting the 28-day compressive strength of Portland cement (CCS) by making use of the 1-day, 3-day and 7-day CCS values. Data taken from a cement plant in Turkey have been employed in the model construction and testing. For implementation, linguistic models were designed based on if-then fuzzy rules. In addition, predictions of these models were compared with results of the regression models. The performance evaluations showed that the linguistic-based fuzzy predictions are very satisfactory in estimating cement strength and the linguistic modeling performs better than regression modeling. © 2008 RILEM

Assessing deformability of siltstone under compressive loading by ultrasonic wave propagation

Determination of stress-strain behaviour of rocks plays a crucial role in understanding the response of rocks during loading. However, it is a tedious, expensive and a time-consuming process to obtain such characteristics both in the laboratory and in situ. On the other hand, non-destructive testing methods such as ultrasonic velocities can produce a reliable and fast solution to indentify state of strain and stress in rocks. The present paper aims to provide insight into the relations amongst ultrasonic wave velocities, amplitudes and deformability properties of porous siltstone under compressive loading. For this purpose, pulse (vP) and shear wave velocities (vS) along with amplitudes (AP - AS), and applied loads with resultant strain were measured simultaneously so that response of siltstone to compression can be evaluated. In addition, initial vP and vS were recorded at the beginning of each test to calculate dynamic elastic properties of the specimens. This provided a comparison between static and dynamic elasticity properties of siltstone. Crack damage associated with the onset of dilation was also investigated. The results showed that there is a non-linear logarithmic relation between velocities and stress-strain level for siltstones.M. Karakus and B. Tutmezhttp://www.ausimm.com.au/publications/epublication.aspx?ID=1300

Regression-based algorithms for exploring the relationships in a cement raw material quarry

Using appropriate raw materials for cement is crucial for providing the required products. Monitoring relationships and analyzing distributions in a cement material quarry are important stages in the process. CaO, one of the substantial chemical components, is included in some raw materials such as limestone and marl; furthermore, appraising spatial assessment of this chemical component is also very critical. In this study, spatial evaluation and monitoring of CaO concentrations in a cement site are considered. For this purpose, two effective regression-based models were applied to a cement quarry located in Turkey. For the assessment, some spatial models were developed and performance comparisons were carried out. The results show that the regression-based spatial modelling is an efficient methodology and it can be employed to evaluate spatially varying relationships in a cement quarry

Measure of uncertainty in regional grade variability

Because the geological events are neither homogeneous nor isotropic, the geological investigations are characterized by particularly high uncertainties. This paper presents a hybrid methodology for measuring of uncertainty in regional grade variability. In order to evaluate the fuzziness in grade values at ore deposit, point cumulative semimadogram (PCSM) measure and a metric distance have been employed. By using the experimental PCSMs and their linear models, measures of fuzziness have been carried out for each location. Finally, an uncertainty map, which defines the regional variation of the uncertainty in different categories, has been composed