Numerical Simulation of Solute Transport in Saturated Porous Media with Bounded Domains

This work presents the first attempt to develop unconditionally stable, implicit finite difference solutions of one-sided spatial fractional advection-dispersion equation (s-FADE) by imposing the nonzero Dirichlet boundary condition (ND BC) or the nonzero fractional Robin boundary condition (NFR BC) at inlet boundary and the zero fractional Neumann boundary condition (ZFN BC) at outlet boundary. The results of the numerical studies performed using artificial solute transport parameters demonstrated that the numerical solution with the NFR BC as the inlet boundary produced much more realistic concentration values. The numerical solution with the NFR BC at the inlet boundary was capable of correctly describing the Fickian and non-Fickian behaviors of the solute transport at different α values, and it had the relatively same accuracy at different numbers of the spatial nodes. Also, the practical application of the numerical solution with the NFR BC as the inlet boundary was investigated by conducting tracer experiments in homogeneous and heterogeneous soil columns. According to the obtained results, this numerical solution described well solute transport in the homogenous and heterogeneous soils. The α values of the homogeneous and heterogeneous soils were obtained in the ranges of 1.849–1.999 and 1.248–1.570, respectively, which were in excellent agreement with the physical properties of the soils. In a nutshell, the numerical solution of the s-FADE with the NFR BC as the inlet boundary can be successfully applied to describe the solute transport in the homogeneous and heterogeneous soils with bounded spatial domains.     

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.     

Automatic calibration of HEC‐HMS using single‐objective and multi‐objective PSO algorithms

This study presents single‐objective and multi‐objective particle swarm optimization (PSO) algorithms for automatic calibration of Hydrologic Engineering Center‐ Hydrologic Modeling Systems rainfall‐runoff model of Tamar Sub‐basin of Gorganroud River Basin in north of Iran. Three flood events were used for calibration and one for verification. Four performance criteria (objective functions) were considered in multi‐objective calibration where different combinations of objective functions were examined. For comparison purposes, a fuzzy set‐based approach was used to determine the best compromise solutions from the Pareto fronts obtained by multi‐objective PSO. The candidate parameter sets determined from different single‐objective and multi‐objective calibration scenarios were tested against the fourth event in the verification stage, where the initial abstraction parameters were recalibrated. A step‐by‐step screening procedure was used in this stage while evaluating and comparing the candidate parameter sets, which resulted in a few promising sets that performed well with respect to at least three of four performance criteria. The promising sets were all from the multi‐objective calibration scenarios which revealed the outperformance of the multi‐objective calibration on the single‐objective one. However, the results indicated that an increase of the number of objective functions did not necessarily lead to a better performance as the results of bi‐objective function calibration with a proper combination of objective functions performed as satisfactorily as those of triple‐objective function calibration. This is important because handling multi‐objective optimization with an increased number of objective functions is challenging especially from a computational point of view. Copyright © 2012 John Wiley & Sons, Ltd.     

Dry and water-stable aggregates in different cultivation systems of arid region soils

Soil aggregate stability has been known as one of the most important soil properties which is influenced by cultivation system. This study investigates the effect of different cultivation systems on aggregate stability indices in two statuses of dry (DSA?>?0.25 mm) and wet (WSA?>?0.25 mm). The study was done in six cultivation systems consisting wheat, barley, maize, alfalfa, fallow, and plowed farms. The results showed that aggregate stability indices affected significantly by the type of cultivation system. In contrast, no meaningful effect of soil depth (0–10 and 10–20 cm) on selected soil properties was observed. In addition, soil primary particles as well as organic carbon differed significantly between the cultivation systems. Wheat and alfalfa farms consisted of larger aggregates, while water-stable aggregate for wheat found to be in a greater degree. Moreover, wheat and barley showed the highest contents of organic carbon. The results of WSA?>?0.25 mm indicated that the correlation coefficients for sand, silt, clay, and organic carbon contents were ?0.67, 0.74, 0.12, and 0.70, respectively. Compared to the DSA?>?0.25 mm, the effect of soil organic carbon on the WSA?>?0.25 mm was arisen while the influence of clay fraction reduced.     

A zinc oxide-coated nanoporous carbon adsorbent for lead removal from water: Optimization, equilibrium modeling, and kinetics studies

A zinc oxide-coated nanoporous carbon sorbent was prepared by acid modification and ZnO functionalization of mesoporous carbon. The synthesized materials, such as mesoporous carbon, oxidized mesoporous carbon and zinc oxide-coated nanoporous carbon, were characterized by nitrogen adsorption–desorption analysis, Fourier transform infrared spectra, scanning electron microscopy, and transmission electron microscopy. ZnO on oxidized mesoporous carbon gradually increased with increase in the number of cycles. Furthermore, the effects of agitation time, initial metal ions concentration, adsorbent dose, temperature and pH on the efficiency of Pb(II) ion removal were investigated as the controllable factors by Taguchi method. The value of correlation coefficients showed that the equilibrium data fitted well to the Langmuir isotherm. Among the adsorbents, zinc oxide-coated nanoporous carbon showed the largest adsorption capacity of 522.8 mg/g (2.52 mmol/g) which was almost close to that of the zinc oxide-coated (2.38 mmol/g), indicating the monolayer spreading of ZnO onto the oxidized mesoporous carbon. The results of the present study suggest that ZnO-coated nanoporous carbon can be effectively used for Pb(II) adsorption from aqueous solution, whereas a part of acidic functional groups may be contributed to binding the Pb(II) for the oxidized mesoporous carbon and mesoporous carbon. Kinetic studies indicated that the overall adsorption process of Pb(II) followed the pseudo-second-order model. The ZnO-coated nanoporous carbon was regenerated and found to be suitable of reuse of the adsorbent for successive adsorption–desorption cycles without considerable loss of adsorption capacity.     

Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach

Landslide is a natural disaster that threatens human lives and properties worldwide. Numerous have been conducted on landslide susceptibility mapping (LSM), in which each has attempted to improve the accuracy of final outputs. This study presents a novel region-partitioning approach for LSM to understand the effects of partitioning a focused region into smaller areas on the prediction accuracy of common regression models. Results showed that the partitioning of the study area into two regions using the proposed method improved the prediction rate from 0.77 to 0.85 when support vector machine was used, and from 0.87 to 0.88 when logistic regression model was utilized. The spatial agreements of the models were also improved after partitioning the area into two regions based on Shannon entropy equations. Our comparative study indicated that the proposed method outperformed the geographically weighted regression model that considered the spatial variations in landslide samples. Overall, the main advantages of the proposed method are improved accuracy and the reduction of the effects of spatial variations exhibited in landslide-conditioning factors.