This paper investigates the suitability of groundwater for domestic and agricultural use in the Rafsanjan plain, southwest part of the Daranjir–Saghand basin, Iran. Fifty-five groundwater samples were collected and analyzed by six methods including the water quality index (WQI), Schoeller diagram, irrigation water quality (IWQ) parameters, Piper diagram, US salinity diagram and Wilcox diagram. The spatial distribution maps of chemical parameters and groundwater quality indices were plotted using the IDW method in GIS. The results showed a low concentration of major ions in the southeastern part and a high concentration from the central part towards northwestern part of the plain. The concentration of major ions in groundwater was strongly affected by groundwater flow, geological setting and the existence of the evaporative layers in the studied area. Moreover, results revealed that most of samples exceeded the acceptable limits recommended by the WHO and ISIRI1053 standards for domestic and agricultural purposes. In most of the wells, groundwater was classified into saline and very hard categories. The analyses based on WQI values indicated that above 87% of water samples were unsuitable for drinking purposes. IWQ parameters expressed that 85%, 67%, 32%, 51%, 43% and 50% of samples had EC > 3000, Na% > 60%, MAR > 50%, KR > 1, SAR > 9 and Cl⁻ > 350, respectively, which were unsuitable for irrigation use. The dominant hydrochemical facies of water was Na–Cl–SO4 type, and 63% and 22.8% of samples were categorized as C4S4 and C4S3 class, with very high-salinity–high-sodium hazards and very high-salinity–high-sodium hazards, respectively. It indicated that most irrigated lands in this study area were affected by different levels of salinity and sodicity hazards that caused decreases in plant growth and crop productivity. The results can assist decision-makers and planners in prioritizing groundwater resources management in the region.

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

This paper presents the development of a spatial model for the allocation of the Urban Search and Rescue (USAR) operation by computing the priority index of damaged areas (PI-USAR model). The relevant prioritization criteria were identified through a literature review and interviews with 30 disaster managers. The relative importance of these criteria was computed as weights using an analytic hierarchy process (AHP) method; the criteria were combined based on AHP rules and spatial multi-criteria decision-making analysis in geographic information system (GIS). The PI-USAR model was applied in the case study area of the Bam city in Iran and a priority map was produced indicating four highly prioritized areas. The model was validated by comparing the obtained priority map with the actual priority map (APM) using fuzzy inference system and relative operating characteristic methods. The result suggested a good fit between the APM and the model's output. Sensitivity analyses were performed using the map removal and the single parameter methods. With the uncertainties and complexities that are inherent to the spatial data, spatial modelling and the earthquake phenomenon itself, the PI-USAR model offers some utility to disaster managers in understanding the significance of each criterion in the decision-making process and in identifying the highly prioritized areas for the allocation of USAR operations. However, its utility is best exploited in conjunction with other complementary sources of field data based on the immediate post-disaster situation.