Urban steeplands in the tropics: an environment of accelerated erosion

The developing countries at present have a high urban growth rate that is likely to continue for at least another quarter-century. In addition, many of these urban centres are located in the Neogene plate boundary zones and are subject to multiple earthquake and volcanic hazards. Slope failures and accelerated surface and channel erosion are particularly severe in cities near active plate margins, and in areas affected also by tropical cyclones. We discuss two extreme cases: Singapore and Kingston (Jamaica). Singapore is located in a stable environment and the urbanization related problems of flood and slope instability have been reduced by proper building and drainage practices at a considerable cost. In Kingston, the external disturbances are repetitive, large-scale, and very difficult to control. The fast-growing cities in the tropics need to be carefully monitored, especially when located in an unstable physical environment. This revised version was published online in July 2006 with corrections to the Cover Date.     

Heavy metal pollution and human health risk assessment for exposure to surface soil of mining area: a comprehensive study

Climate change projections indicate an increase in intense rainfall events with consequent river flooding, which could lead to devastating natural disaster     

REE geochemistry of core sediments of Cauvery delta,India for provenance studies

Acta Geochimica - The rare earth element (REE) geochemical composition of sediments from two cores were used to investigate the provenances of the Late Pleistocene to Holocene sediments of Cauvery...     

A Genetic Programming–Based Model for Colloid Retention in Fractures

Understanding the behavior of colloids in groundwater is critical as some are pathogenic while others may facilitate or inhibit the transport of dissolved contaminants. Colloid behavior in saturated fractured aquifers is governed by the physical and chemical properties of the groundwater-particle-fracture system. The interaction between these properties is nonlinear, and there is a need for a mathematical model describing the relationship between them to advance the mechanistic understanding of colloid transport in fractures and facilitate modeling in fractured environments. This paper coupled genetic programming and linear regression within a multigene genetic programming framework to develop a robust mathematical model describing the relationship between colloid retention in fractures and the physical and chemical parameters that describe the system. The data employed for model development and validation were collected from a series of 75 laboratory-scale colloid tracer experiments conducted under a range of conditions in three laboratory-induced discrete dolomite fractures and their epoxy replicas. The model sufficiently reproduced the observed data with coefficients of determination (R²) of 0.92 and 0.80 for model development and validation, respectively. A cross-validation demonstrated the model generality to 86% of the observed data. A variance-based global sensitivity analysis confirmed that attachment is the primary retention mechanism in the systems employed in this work. The model developed in this study provides a tool describing colloid retention in factures, which furthers the understanding of groundwater-particle-fracture system conditions contributing to the retention of colloids and can aid in the design of groundwater remediation strategies and development of groundwater management plans.     

The Relationship between Fe Mineralization and Magnetic Basement Faults using Multifractal Modeling in the Esfordi and Behabad Areas (BMD), Central Iran

Multifractal modeling is a mathematical method for the separation of a high potential mineralized background from a non-mineralized background. The Concentration-Distance to Fault structures (C-DF) fractal model and the distribution of the known iron (Fe) deposits/mines seen in the Esfordi and Behabad 1:100,000 sheets from the Bafq region of central Iran are used to distinguish Fe mineralization based on their distance to magnetic basement structures and surface faults, separately, using airborne geophysical data and field surveys. Application of the C-DF fractal model for the classification of Fe mineralizations in the Esfordi and Behabad areas reveals that the main ones show a correlation with their distance from magnetic basement structures. Accordingly, the distances of Fe mineralizations with grades of Fe higher than 55% )43% < Fe ≤ 60%) are located at a distance of less than 1 km, whereas for surfacial faults with grades of 43% ≤ Fe ≤ 60%, the distances are 3162< DF ≤ 4365 m from the faults. Thus, there is a positive relationship between Fe mineralization and magnetic basement structures. Also, the proximity evidence of Precambrian high-grade Fe mineralization related to magnetic basement structures indicates syn-rifting tectonic events. Finally, this C-DF fractal model can be used for exploration of magmatic and hydrothermal ore deposits.     

3D inversion of DC data using artificial neural networks

In this paper, we investigate the applicability of artificial neural networks in inverting three-dimensional DC resistivity imaging data. The model used to produce synthetic data for training the artificial neural network (ANN) system was a homogeneous medium of resistivity 100 Ωm with an embedded anomalous body of resistivity 1000 Ωm. The different sizes for anomalous body were selected and their location was changed to different positions within the homogeneous model mesh elements. The 3D data set was generated using a finite element forward modeling code through standard 3D modeling software. We investigated different learning paradigms in the training process of the neural network. Resilient propagation was more efficient than any other paradigm. We studied the effect of the data type used on neural network inversion and found that the use of location and the apparent resistivity of data points as the input and corresponding true resistivity as the output of networks produces satisfactory results. We also investigated the effect of the training data pool volume on the inversion properties. We created several synthetic data sets to study the interpolation and extrapolation properties of the ANN. The range of 100–1000 Ωm was divided into six resistivity values as the background resistivity and different resistivity values were also used for the anomalous body. Results from numerous neural network tests indicate that the neural network possesses sufficient interpolation and extrapolation abilities with the selected volume of training data. The trained network was also applied on a real field dataset, collected by a pole-pole array using a square grid (8 ×8) with a 2-m electrode spacing. The inversion results demonstrate that the trained network was able to invert three-dimensional electrical resistivity imaging data. The interpreted results of neural network also agree with the known information about the investigation area.