Assessing and Modeling the Impacts of Wetland Land Cover Changes on Water Provision and Habitat Quality Ecosystem Services

Natural Resources Research - Understanding the spatial–temporal dynamics of wetland land cover (LC) changes and their impacts on ecosystem services (ESs) is essential for wetland conservation...     

A stochastic framework to assess the performance of flood warning systems based on rainfall‐runoff modeling

Nowadays, Flood Forecasting and Warning Systems (FFWSs) are known as the most inexpensive and efficient non‐structural measures for flood damage mitigation in the world. Benefit to cost of the FFWSs has been reported to be several times of other flood mitigation measures. Beside these advantages, uncertainty in flood predictions is a subject that may affect FFWS's reliability and the benefits of these systems. Determining the reliability of advanced flood warning systems based on the rainfall–runoff models is a challenge in assessment of the FFWS performance which is the subject of this study. In this paper, a stochastic methodology is proposed to provide the uncertainty band of the rainfall–runoff model and to calculate the probability of acceptable forecasts. The proposed method is based on Monte Carlo simulation and multivariate analysis of the predicted time and discharge error data sets. For this purpose, after the calibration of the rainfall–runoff model, the probability distributions of input calibration parameters and uncertainty band of the model are estimated through the Bayesian inference. Then, data sets of the time and discharge errors are calculated using the Monte Carlo simulation, and the probability of acceptable model forecasts is calculated by multivariate analysis of data using copula functions. The proposed approach was applied for a small watershed in Iran as a case study. The results showed using rainfall–runoff modeling based on real‐time precipitation is not enough to attain high performance for FFWSs in small watersheds, and it seems using weather forecasts as the inputs of rainfall–runoff models is essential to increase lead times and the reliability of FFWSs in small watersheds. Copyright © 2013 John Wiley & Sons, Ltd.     

Solving land-use suitability analysis and planning problem by a hybrid meta-heuristic algorithm

The aim of Land-use Suitability Analysis and Planning Problem (LSAPP) is to identify the most suitable parcels of land for future land-uses considering several conflicting criteria. LSAPP can be modeled using a variant of a well-known combinatorial optimization problem called Quadratic Assignment Problem (QAP). In this paper, a multi-objective mathematical model is developed for LSAPP based on QAP modeling. The large-size instances of the proposed multi-objective mathematical model are difficult to solve in a reasonable CPU time using exact algorithms. So, an efficient three-phase hybrid solution procedure is proposed. In the first phase, the compensatory objectives are integrated using Analytic Hierarchy Process (AHP) and Decision-Making Trial and Evaluation Laboratory. Then, based on the aforementioned suitability objective function and other spatial objectives and constraints, a multi-objective LSAPP is constructed. Finally, a hybrid multiple objective meta-heuristic algorithm is proposed to solve the LSAPP. The core of the proposed algorithm is based on Scatter Search while Tabu Search and Variable Neighborhood Search are also utilized. The proposed algorithm is equipped with the concepts of Pareto optimality and Veto Threshold, which improve its efficacy. The proposed algorithm is applied on a real LSAPP case study, in ‘Persian Gulf Knowledge Village’, wherein its performance is compared with a well-known evolutionary computation algorithm called Vector Evaluated Genetic Algorithm (VEGA) using comprehensive statistical analysis. A survey on time complexity of the proposed algorithm is also accomplished. The results show that MOSVNS is significantly superior to VEGA both in single and in multi-objective modes. Furthermore, analysis of time complexity of the proposed algorithm shows that it is of polynomial time and can be applied to significantly larger problems with multiple compensatory and non-compensatory objectives.     

Biofiltration of Hexane Vapor: Experimental and Neural Model Analysis

Biofiltration is a commonly practiced biological technique to remove volatile compounds from waste gas streams. From an industrial view‐point, biofilter (BF) operation should be flexible to handle temperatures and inlet load (IL) variations. A compost BF was operated at different temperatures (30–45°C) and at various inlet loading rates (ILR; 8–598 g m^?3 h^?1) under intermittent loading conditions. Complete removal of n‐hexane was observed at 30 and 35°C at ILRs up to 330 g m^?3 h^?1. Besides, 20–75% of the pollutant was removed at 40°C, corresponding to the different ILs applied to the BF. Increasing the temperature to 45°C decreased the removal efficiency (RE) significantly. A feed forward neural network was used to predict the RE of BF with temperature and ILR as the input variables. The experimental data was divided into training (2/3) and test datasets (1/3). The best structure of neural network was obtained by trial and error on the basis of the least differences between predicted and experimental values, as ascertained from their coefficient of regression (R²) values. The modeling results showed that a multilayer network with the topology 2?10?1 was able to predict BF performance effectively with R²‐value of 0.995 for the test data. The results from this study showed the predicting capability of ANNs which can be considered as an alternative for conventional knowledge‐based models.     

Modification of MCM‐41 with Anionic Surfactant: A Convenient Design for Efficient Removal of Cationic Dyes from Wastewater

The potential of MCM‐41 for the removal of cationic dyes from water solution was evaluated using sodium dodecyl sulfate (SDS) for the surface modification of this mesoporous material. Admicelle structures formed on the surface of the calcined MCM‐41 are capable of removing organic pollutants and cationic species from water environment. The structural, textural, and surface chemical characteristics of the prepared SDS‐modified MCM‐41 (SDS‐MCM‐41) were studied. The adsorption capacity of SDS‐MCM‐41 was evaluated for methylene blue (MB) as a target cationic dye. Equilibrium adsorption isotherm data were manipulated employing nonlinear regression analysis. The Langmuir, Freundlich, and Sips isotherm models were examined. The adsorption data were well fitted to both Langmuir and Sips isotherm models. The maximum adsorption capacity of SDS‐MCM‐41 for MB, based on Langmuir and Sips models, were 290.8 and 297.3 mg g^?1, respectively. Ethanol was found to be an effective solvent for partial regeneration of the adsorbent.     

A Complete Microjansky Radio Survey

The Phoenix Deep Survey is an ongoing multi-wavelength survey of a 2^° diameter field aimed at studying the properties of the sub-mJy and μJy radio population. Here, we present the latest 1.4 GHz observations of this field. The new data, reaching a 5 σ flux level of 45 μJy at the centre of a 50′ diameter field, comprise more than 700 sources with flux densities less than 1 mJy (187 of which have S_1.4 < 100 μJy). This provides one of the deepest radio (1.4 GHz) surveys currently available. The 1.4 GHz source counts are presented and show a flattening down to the 50 μJy level. At flux densities around 300 μJy there are indications that the sources detected may exhibit higher clustering than those observed at higher flux levels. This suggests that deep radio surveys could be useful for studies of large-scale structure but it also presents a warning for the representativity of sources in deep pencil-beam radio surveys. The study of the optical counterparts of the μJy population seems to indicate that the median R magnitude starts to decrease below 100 μJy. Spectroscopic classification of a sample of sources in this survey confirms the trend for an increasing fraction of star-forming galaxies over other systems down to ～ 100 μJy.     

Assessment of agricultural groundwater users in Iran: a cultural environmental bias

Many environmental problems are rooted in human behavior. This study aimed to explore the causal effect of cultural environmental bias on ‘sustainable behavior’ among agricultural groundwater users in Fars province, Iran, according to Klockner’s comprehensive model. A survey-based research project was conducted to gathering data on the paradigm of environmental psychology. The sample included agricultural groundwater users (n = 296) who were selected at random within a structured sampling regime involving study areas that represent three (higher, medium and lower) bounds of the agricultural-groundwater-vulnerability spectrum. Results showed that the “environment as ductile (EnAD)” variable was a strong determinant of sustainable behavior as it related to groundwater use, and that EnAE had the highest causal effect on the behavior of agricultural groundwater users. The adjusted model explained 41% variance of “groundwater sustainable behavior”. Based on the results, the groundwater sustainable behaviors of agricultural groundwater users were found to be affected by personal and subjective norm variables and that they are influenced by casual effects of the “environment as ductile (EnAD)” variable. The conclusions reflect the Fars agricultural groundwater users’ attitude or worldview on groundwater as an unrecoverable resource; thus, it is necessary that scientific disciplines like hydrogeology and psycho-sociology be considered together in a comprehensive approach for every groundwater study.     

Assessing prediction models of advance rate in tunnel boring machines—a case study in Iran

Tunnel boring machine applies in tunnel construction and in mining operation. During the last years, different methods have been introduced to analyze and assess suitable operations of digging systems presented. These methods are divided in two groups: (1) the first group is based on mathematical equations and shear strength applied on each cutter, (2) the second group is based on databanks and experimental relationship. This paper compares and analyzes two experimental methods as introduced by Barton and Norwegian Institute of Technology (NTNU) as well as using a mathematic model introduced by Colorado School of Mines and analyzed the validity scope of each of them. A case study is made in the 16-km Karaj–Tehran water supply tunnel. At the end, it is concluded that mathematical models are not suitable because they are highly dependent on the results of special laboratory tests; also, it attends less to rock mass characteristics. In jointed or nonhomogen rocks, as well as in this project with less value of laboratory data, using Barton model is more creditable. It enjoys high ability for definite measurement. Also, NTNU model attend to machine parameters and in case of availability of laboratory tests data, NTNU model is a suitable method. According to the available information and executing conditions of Karaj–Tehran water supply tunnel project including geology of area, experimental parameters, etc, the Barton method is more valid than the other methods.     

Entropy and statefinder diagnosis in chameleon cosmology

In this paper, the generalized second law (GSL) of thermodynamics and entropy is revisited in the context of cosmological models with bouncing behavior such as chameleon cosmology where the boundary of the universe is assumed to be enclosed by the dynamical apparent horizon. From a thermodynamic point of view, to link between thermodynamic and geometric parameters in cosmological models, we introduce “entropy rate of change multiplied by the temperature” as a model independent thermodynamic state parameter together with the well known {r,s} statefinder to differentiate the dark energy models.     

Geometric and thermodynamic properties in Gauss-Bonnet gravity

In this paper, the generalized second law (GSL) of thermodynamics and entropy is revisited in the context of cosmological models in Gauss-Bonnet gravity with the boundary of the universe is assumed to be enclosed by the dynamical apparent horizon. The model is best fitted with the observational data for distance modulus. The best fitted geometric and thermodynamic parameters such as equation of state parameter, deceleration parameter and entropy are derived. To link between thermodynamic and geometric parameters, the “entropy rate of change multiplied by the temperature” as a model independent thermodynamic state parameter is also derived. The results show that the model is in good agreement with the observational analysis.