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Recognizing potential sites for landfill has increasingly become an important waste management strategy around the world. This study aims to determine municipal solid waste (MSW) landfills in SaharKhiz Region located in Gilan by comparing Fuzzy logic and Boolean logic. Fuzzy logic, which has been used in this research, is based on weighted linear combination (WLC); however, the utilized Boolean logic is considered only to determine the accuracy and validity of every method. At first, the Boolean logic using a geographical information system (GIS) is used to recognize potential and excluded zones, based on zero and one value system. In the next phase, Fuzzy logic is used, between zero and one, to standardize information layers, based on their type (increasing or decreasing). The final weight of every layer was determined using the analytical hierarchy process. Finally, the WLC method was used to integrate layers in the GIS environment to provide the final site suitability map in five classes of Fuzzy membership degree. The results show that Fuzzy logic, based on WLC, has more flexibility to resolve conflicts of human judgment, and it also has higher accuracy than Boolean logic in the selection of optimal landfill sites for MSW in SaharKhiz Region, Gilan Province, based on ecological and socioeconomic parameters.

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Due to the increasing population and the growing need for providing energy from a clean and non-fossil resource, there is an increasing interest in nuclear energy all over the world. As a fact, environmental capability evaluation should be considered for establishment of new nuclear power plants, not only to increase the safety against environmental risks, but also to achieve sustainable development. This study focuses on environmental capability evaluation for the nuclear power plant site selection in the coastal area of Gilan Province (Sahar Khiz). Considering the natural potentials and limitations of the area, the existing laws and regulations set by the International Atomic Energy Agency, United States Nuclear Regulatory Commission and Department of Environment, ecological and socioeconomic criteria were the main object of this study. At first, the Boolean logic method was used to prepare layered maps and identify selected zones, using geographical information system (GIS) and the ''AND'' operator. Then, the fuzzy logic method based on weighted linear combination (WLC) was applied for site selection of the new nuclear power plant. Four main criteria (physical, biological, social and economic) and 15 sub-criteria have been chosen for site selection process, from all selected criteria and sub-criteria. After preparing the raster maps of the ecological and socioeconomic sub-criteria, the fuzzy logic was used to standardize data layers between zero and one. Then, a relative weight was assigned to each criterion using Expert Choice software and the analytical hierarchy process (AHP) method. At the end, to combine all subcriteria, fuzzy logic method based on WLC was used in GIS environment, and the final map of environmental capability was prepared. The results of this study were compared to the results using the Boolean logic method, showing high coverage between the two methodologies, while the fuzzy logic method based on WLC combined with the AHP method had more flexibility and more accuracy comparing with the Boolean logic method.

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Identifying areas prone to ooding is a key step in ood hazard management. The purpose of this study is to develop and present a novel ood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fteen climatic and geo-environmental variables were used as inputs into all ood susceptibility models: altitude, aspect, slope, plan curvature, pro le curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing ood eld survey and other information available for the analyzed area, a total of 118 ood locations were identi ed as potentially prone to ooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the altitude and distance from the river are the most important variables for assessing ooding susceptibility.

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