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In this study, an efficient approach is suggested to prioritize the natural gas storage in existing underground reservoirs. The paper uses expert knowledge and integrates the Geospatial Information System (GIS) with spatial multi criteria decision making .The proposed method is based on TOPSIS as well as Hierarchical Additive Weighting Method. In order to consider different options, the most important possible criteria have been considered in this study, including: reservoir characteristics, distance from the center of gravity taking, distance from production centers, distance from the country's road network, the temperature around the reservoir areas, environmental characteristics of the reservoir, consumption gas in areas around the reservoir, population density, and the number of important industrial centers covered by each of the reservoirs. To evaluate the results of research, results of implemented methods were compared with each others and according to evaluation obtained from several experts the final prioritization of different reservoirs due to their performances has been done. The results indicate that TOPSIS is more precise than Hierarchical Additive Weighting Method, and conform to experts opinions about prioritization of natural gas storage in underground reservoirs.

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Cultivation of agricultural land in the basin Zarineh has changed over the years and as one of the main reasons for reducing the inflow to Lake Urmia has been raised. Due to the possible development, Estimation of prospects of changes in the basin can play in key role in taking  effective decisions and provide guidelines for dealing with the environmental crisis Lake Urmia is facing with. In this study has been tried to extract and assess the historical changes in land use in the Zarineh  basin using satellite images and a projection of the future land use changes has been provided. In this regard, a combined of CA-MARKOV method was used. Results show that between 2000 and 2013, on average, most of changes lean toward  increase in land use, such as irrigated agriculture (40%) and gardens (57%) and also to reduce pasture land (5%) and dryland farming (10%). This trend has also been observing  for horizon of year 2020.

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 Wireless sensors deployment is considered as one of the major and fundamental steps of wireless sensor networks (WSNs) design. One of the main challenges of sensors deployment is to find a trade-off between conflicting and competing objectives of the WSN including network coverage and lifetime under connectivity constraints. Besides, decomposition is a basic method in traditional multi-objective optimization and in recent decades, it has also been used for optimizing multi-objective evolutionary problems. In this paper, a constrained Pareto-based multi-objective evolutionary approach based on decomposition (CPMEA/D) is proposed for solving the sensors optimal deployment problem in a WSN. The aim of this approach is to decompose the multi-objective optimization problem into a number of scalar optimization subproblems and then to optimize them simultaneously for finding the Pareto optimal layouts in which the network coverage is maximized and the sensors energy consumption is minimized while the connectivity between each sensor node and the high energy communication node (i.e. sink) is maintained. In this paper, the comparison of the common performance metrics indicates that the proposed approach has made significant improvements on the overall performance of the CPMEA. Moreover, the simulation results on a WSN test instance have shown the superiority of the proposed approach (i.e. CPMEA/D) over the CPMEA and a diverse set of high quality designed networks has been provided to facilitate decision maker’s choices.