Improving the efficiency of ant algorithms using adaptive refinement: Application to storm water network design

The ant algorithm is a new evolutionary optimization method proposed for the solution of discrete combinatorial optimization problems. Many engineering optimization problems involve decision variables of continuous nature. Application of the ant algorithm to the optimization of these continuous problems requires discretization of the continuous search space, thereby reducing the underlying continuous problem to a discrete optimization problem. The level of discretization of the continuous search space, however, could present some problems. Generally, coarse discretization of the continuous design variables could adversely affect the quality of the final solution while finer discretization would enlarge the scale of the problem leading to higher computation cost and, occasionally, to low quality solutions. An adaptive refinement procedure is introduced in this paper as a remedy for the problem just outlined. The method is based on the idea of limiting the originally wide search space to a smaller one once a locally converged solution is obtained. The smaller search space is designed to contain the locally optimum solution at its center. The resulting search space is discretized and a completely new search is conducted to find a better solution. The procedure is continued until no improvement can be made by further refinement. The method is applied to a benchmark problem in storm water network design discipline and the results are compared with those of existing methods. The method is shown to be very effective and efficient regarding the optimality of the solution, and the convergence characteristics of the resulting ant algorithm. Furthermore, the method proves itself capable of finding an optimal, or near-optimal solution, independent of the discretization level and the size of the colony used.     

Analyzing long-term spatial variability of blue and green water footprints in a semi-arid mountainous basin with MIROC-ESM model (case study: Kashafrood River Basin,Iran)

Atmospheric modeling is considered an important tool with several applications such as prediction of air pollution levels, air quality management, and environmental impact assessment studies. Therefore, evaluation studies must be continuously made, in order to improve the accuracy and the approaches of the air quality models. In the present work, an attempt is made to examine the air pollution model (TAPM) efficiency in simulating the surface meteorology, as well as the SO₂ concentrations in a mountainous complex terrain industrial area. Three configurations under different circumstances, firstly with default datasets, secondly with data assimilation, and thirdly with updated land use, ran in order to investigate the surface meteorology for a 3-year period (2009–2011) and one configuration applied to predict SO₂ concentration levels for the year of 2011.The modeled hourly averaged meteorological and SO₂ concentration values were statistically compared with those from five monitoring stations across the domain to evaluate the model’s performance. Statistical measures showed that the surface temperature and relative humidity are predicted well in all three simulations, with index of agreement (IOA) higher than 0.94 and 0.70 correspondingly, in all monitoring sites, while an overprediction of extreme low temperature values is noted, with mountain altitudes to have an important role. However, the results also showed that the model’s performance is related to the configuration regarding the wind. TAPM default dataset predicted better the wind variables in the center of the simulation than in the boundaries, while improvement in the boundary horizontal winds implied the performance of TAPM with updated land use. TAPM assimilation predicted the wind variables fairly good in the whole domain with IOA higher than 0.83 for the wind speed and higher than 0.85 for the horizontal wind components. Finally, the SO₂ concentrations were assessed by the model with IOA varied from 0.37 to 0.57, mostly dependent on the grid/monitoring station of the simulated domain. The present study can be used, with relevant adaptations, as a user guideline for future conducting simulations in mountainous complex terrain.     

Socio-hydrological framework for investigating farmers’ activities affecting the shrinkage of Urmia Lake; hybrid data mining and agent-based modelling

ABSTRACT

Developing a general framework to capture the complexities associated with the non-linear and adaptive nature of farmers facing water resources scarcity is a challenging problem. This paper integrates agent-based modelling (ABM) and a data mining method to develop a hybrid socio-hydrological framework to provide future insights for policy-makers. The data associated with the farmers’ main characteristics were collected through field surveys and interviews. Afterwards, the association rule was employed to discover the main patterns representing the farmers’ agricultural decisions. The discovered patterns were then used as the behavioural rules in ABM to simulate the agricultural activities. The proposed framework has been was applied to explore the interactions between agricultural activities and the main river feeding the Urmia-Lake, Iran. The outcomes indicate that farmers’ acquisitive traits and belongings have significant impacts on their socio-hydrological interactions. The reported values of the efficiency criteria may support the satisfactory performance of the proposed framework.     

Application of various methods for 2D inverse modeling of residual gravity anomalies

This paper presents a neural network approach to determine 2D inverse modeling of a buried structure from gravity anomaly profile. The results of the applied neural network method are compared with the results of two other methods, least-squares minimization and the simple method. Sphere, horizontal cylinder and vertical cylinder and their gravity effects are considered as the synthetic models and the synthetic data, respectively. The synthetic data are also corrupted with noise to evaluate the capability of the methods. Then the Dehloran bitumen map in Iran is chosen as a real data application. Anomaly value of the cross-section, which is taken from the gravity anomaly map of Dehloran bitumen, is very close to those obtained from these methods.     

Effect of inertia nonlinearity on dynamic response of an asymmetric building equipped with tuned mass dampers

This study investigates the effect of nonlinear inertia on the dynamic response of an asymmetric building equipped with Tuned Mass Dampers (TMDs). In the field of structural engineering, many researchers have developed models to study the behavior of nonlinear TMDs, but the effect of nonlinear inertia has not received as much attention for asymmetric buildings. To consider nonlinear inertia, the equations of motion are derived in a local rotary coordinates system. The displacements and rotations of the modeled building and TMDs are defined by five-degree-of-freedom (5-DOFs). The equations of motion are derived by using the Lagrangian method. Also in the proposed nonlinear model, the equations of motion are different from a conventional linear model. In order to compare the response of the proposed nonlinear model and a conventional linear model, numerical examples are presented and the response of the modeled buildings are derived under harmonic and earthquake excitations. It is shown that if the nonlinear inertia is considered, the response of the modeled structures changes and the conventional linear approach cannot adequately model the dynamic behavior of the asymmetric buildings which are equipped with TMDs.     

The potential for using the sycamore (Platus orientalis) leaves in manufacturing particleboard

Developing value-added products from any underutilized non-woody or woody material could play a critical role in economical development as well as forest resource management of any country. The objective of this research was to evaluate the potential of using the sycamore leaves in production of particleboard. Variable factors were as board density (0.6 and 0.7 g/cm³), press time (4 and 6 min), and press temperature (140 and 160 °C). Some chemical properties of the sycamore leaves (cellulose, holocellulose, lignin and ash contents, alcohol–benzene solubility, 1 % sodium hydroxide solubility, hot- and cold-water solubility), mechanical (modulus of rupture, modulus of elasticity, and internal bond strength) and physical properties (thickness swelling and water absorption) of the resulting particleboards were determined. Overall results showed that the mechanical properties of all the panels exceed the minimum requirements of European Norm for furniture manufacturing. The mechanical properties of particleboard were improved significantly as board density increased from 0.6 to 0.7 g/cm³ and press time increased from 4 to 6 min. However, the effect of press temperature was not significant (P < 0.05). Conclusively, sycamore leaves, an underutilized waste, can be an alternative raw material for particleboard industry and resulting value-added panels owing to their high mechanical properties and interesting design esthetics could be used for furniture and interior decoration.     

Climate change forecasting in a mountainous data scarce watershed using CMIP5 models under representative concentration pathways

Hydrology cycle of river basins and available water resources in arid and semi-arid regions are highly affected by climate changes. In recent years, the increment of temperature due to excessive increased emission of greenhouse gases has led to an abnormality in the climate system of the earth. The main objective of this study is to survey the future climate changes in one of the biggest mountainous watersheds in northeast of Iran (i.e., Kashafrood). In this research, by considering the precipitation and temperature as two important climatic parameters in watersheds, 14 models evolved in the general circulation models (GCMs) of the newest generation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to forecast the future climate changes in the study area. For the historical period of 1992–2005, four evaluation criteria including Nash–Sutcliffe (NS), percent of bias (PBIAS), coefficient of determination (R ²) and the ratio of the root-mean-square-error to the standard deviation of measured data (RSR) were used to compare the simulated observed data for assessing goodness-of-fit of the models. In the primary results, four climate models namely GFDL-ESM2G, IPSL-CM5A-MR, MIROC-ESM, and NorESM1-M were selected among the abovementioned 14 models due to their more prediction accuracies to the investigated evaluation criteria. Thereafter, climate changes of the future periods (near-century, 2006–2037; mid-century, 2037–2070; and late-century, 2070–2100) were investigated and compared by four representative concentration pathways (RCPs) of new emission scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP8.5. In order to assess the trend of annual and seasonal changes of climatic components, Mann–Kendall non-parametric test (MK) was also employed. The results of Mann–Kendall test revealed that the precipitation has significant variable trends of both positive and negative alterations. Furthermore, the mean, maximum, and minimum temperature values had significant positive trends at 90, 99, and 99.9 % confidence level. On the other hand, in all parts of the Kashafrood Watershed (KW), the average temperature of watershed will be increased up to 0.56–3.3 °C and the mean precipitation will be decreased up to 10.7 % by the end of the twenty-first century comparing to the historical baselines. Also, in seasonal scale, the maximum and minimum precipitations will occur in spring and summer, respectively, and the mean temperature is higher than the historical baseline in all seasons. The maximum and minimum values of the mean temperature will occur in summer and winter, respectively, and the amount of seasonal precipitation in these seasons will be reduced.

     

Enhanced CE-QUAL-W2 model to predict the fate and transport of volatile organic compounds in water body: Gheshlagh reservoir as case study

Intentional and/or accidental volatile organic compound (VOC) spill into water bodies may lead to severe contamination and health problems in water infrastructures. The importance and widespread use of petroleum products and the threats posed by these products on surface water resources in Iran necessitates the access to numerical hydrodynamic and water quality simulation models with appropriate capabilities. Simulation the fate and transport of VOC in both flowing and standing water bodies is a fairly complex problem. In this research, CE-QUAL-W2 model is modified to simulate the fate and transport of VOC [i.e., Methyl tert-butyl ether (MTBE), benzene] in standing and flowing water bodies. The performance of the modified CE-QUAL-W2 model is evaluated in a MTBE pollution spill at Khalife-Tarkhan river along the headwaters of Gheshlagh reservoir, Kordestan, Iran. The results show the modified CE-QUAL-W2 model’s capability to depict the spatial and temporal variation of MTBE in comparison with recorded data from MTBE spill event of Gheshlagh reservoir. Based on the simulation results of modified CE-QUAL-W2 model, reservoir cleanup time in different meteorological and hydrological scenarios is evaluated. The results show Gheshlagh reservoir cleanup time reduced in scenarios that included air temperature reduction, wind speed increasing, and high inflow condition.