Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Catalysis. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iran

Abstract: The aim of the current study was to produce groundwater spring potential map using a bivariate statistical model (frequency ratio) and geographical information system (GIS) in the Taleghan Watershed, Alborz Province, Iran. Firstly, field surveys were done for identifying and springs inventory mapping. In total, 457 springs were identified and mapped in GIS; out of that, 320 (70 %) locations were selected for training and the remaining 137 (30 %) cases were used for the model validation. The effective factors on the groundwater spring such as: slope percent, slope aspect, altitude, topographic wetness index, stream power index, slope length, plan curvature, distance from rivers, distance from roads, distance from faults, lithology, land use, soil hydrology groups, and drainage density were derived from the spatial database. Using the above effective factors, groundwater spring potential mapping was calculated using FR model as a bivariate statistical model, and the results were plotted in Arc GIS. Eventually, the receiver operating characteristic curve was drawn for spring potential map and the area under the curve (AUC) was figured. Validation of results indicated that the frequency ratio model (AUC = 75.99 %) performed fairly good predication accuracy. The results of groundwater spring potential map may be helpful for planners and engineers in water resource management and land use planning.

Energy and exergy analysis of Montazeri Steam Power Plant in Iran

Abstract: This paper aims at investigating steam cycle of Shahid Montazeri Power Plant of Isfahan with individual unit capacity of 200 MW. Using mass, energy, and exergy balance equations, all cycle equipment have been analyzed individually and energy efficiency, exergy efficiency, and irreversibility has been calculated for each of them as required. EES (Engineering Equation Solver) software is used for performing analyses. Values and ratios regarding heat drop and exergy loss have been presented for each equipment in individual tables. The results from the energy analysis show that 69.8% of the total lost energy in the cycle occurs in the condenser as the main equipment wasting energy, while exergy analysis introduces the boiler as the main equipment wasting exergy where 85.66% of the total exergy entering the cycle is lost.

Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility

Abstract: Estimation of landslide susceptibility is still an ongoing requirement for land use management plans. Here, we proposed two novel intelligence hybrid models that rely on an adaptive neuro-fuzzy inference system (ANFIS) and two metaheuristic optimization algorithms, i.e., grey wolf optimizer (GWO) and biogeography-based optimization (BBO), for obtaining a reliable estimate of landslide susceptibility. Sixteen causative factors and 391 historical landslide events from a landslide-prone area of the State of Uttarakhand, northern India, were used to generate a geospatial database. The ANFIS model was employed to develop an initial landslide susceptibility model that was then optimized using the GWO and BBO algorithms. This resulted in two novel models, i.e., ANFIS-BBO and ANFIS-GWO, that benefited from an intelligent approach to automatically and properly adjust the best parameters of the base ANFIS model for the prediction of landslide susceptibilities. The robustness of the models was verified through a large number of runs using different splits of training and validation datasets. Although few differences observed between the predictive capability of the models (AUCANFIS-BBO = 0.95; RMSEANFIS-BBO = 0.316 vs. ACUANFIS-GWO = 0.94; RMSEANFIS-GWO = 0.322), the Wilcoxon signed-rank test indicated a significant difference between the model performances in both training and validation datasets. Overall, our proposed models demonstrated an improved prediction of landslides compared to those achieved in previous studies with other methods. Therefore, these novel models can be recommended for modeling landslide susceptibility, and the modelers can easily tailor their use based on their individual circumstances.