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Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze NO À 3 concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the NO À 3 data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using NO À 3 concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and NO À 3 concentration values. After model training, the AI models are verified by the second NO À 3 concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.

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A comprehensive study has been performed to achieve all-angle self-collimation in basic two-dimensional square array photonic crystals with cylindrical scatterers. Based on plane wave expansion and finite difference time domain analysis for both rod-and hole-type structures, we report on all-angle self-collimation (SC) in the first band of the structure, which results in loss suppression due to out-of-plane scatterings. A lower threshold for index contrast has been obtained to achieve all-angle SC, which offers more design flexibility regarding structural parameters. Furthermore, it has been shown that a minimum and maximum coupling efficiency enhancement of ∼40% and 80% can be achieved for the proposed structure, respectively, by introducing a row of scatterers with proper radius at the input and the output air/photonic crystal interfaces. Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms Optical Engineering 037111-2 March 2015 • Vol. 54(3) Noori, Soroosh, and Baghban: All-angle self-collimation in two-dimensional square array photonic crystals. . . Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms Mina Noori received her BS and MS degrees in electronics engineering (optical integrated circuits) from University of Tabriz, Iran, in 2010 and 2012, respectively. She is currently working toward her PhD degree at Shahid Chamran University, Ahvaz, Iran, and her current research interest includes photonic crystal devices and structures. Mohammad Soroosh received his MS and PhD degrees in electronics engineering from Tarbiat Modares University, Tehran,