Fuzzy multi-criteria decision making model for different scenarios of electrical power generation in Egypt

Comparison of Cash Crop Suitability Assessment Using Parametric, AHP, and FAHP Methods

Cash crops, which include eucalyptus, play an important role in Thailand in wood utilization. Consequently, cash crops have become a significant driving force in land use changes and low crop yield; thus, the development of an accurate cash crop suitability model is needed. The aim of this study is to evaluate the land suitability of cash crops, such as eucalyptus, which is based on Multi Criteria Decision Making (MCDM) in Nakhon Ratchasima Province in Thailand. Parametric, classical Analytical Hierarchy Process (AHP), and fuzzy AHP (FAHP) approaches integrated with Geographic Information Systems (GIS) are compared to accomplish this. Parametric approaches equally allocate importance to all factors. AHP assigns the distribution of important factors using expert opinions. FAHP accounts for the uncertainty in expert opinions, and the triangular (Tri) and trapezoidal (Tra) approaches are compared. The results demonstrated that Trapezoidal Fuzzy AHP (TraFAHP) could classify and map cash crop suitability with 90.16% accuracy, which is a higher overall accuracy than the other approaches that are based on reference map validation. Therefore, we recommend the TraFAHP method for accurately identifying cash crop suitability.

Mapping Socioeconomic Exposure for Flood Risk Assessment in Italy

Detailed spatial representation of socioeconomic exposure and the related vulnerability to natural hazards has the potential to improve the quality and reliability of risk assessment outputs. We apply a spatially weighted dasymetric approach based on multiple ancillary data to downscale important socioeconomic variables and produce a grid data set for Italy that contains multilayered information about physical exposure, population, gross domestic product, and social vulnerability. We test the performances of our dasymetric approach compared to other spatial interpolation methods. Next, we combine the grid data set with flood hazard estimates to exemplify an application for the purpose of risk assessment.

Gaussian Fuzzy Number for STR-DNA Similarity Calculation Involving Familial and Tribal Relationships

We performed locus similarity calculation by measuring fuzzy intersection between individual locus and reference locus and then performed CODIS STR-DNA similarity calculation. The fuzzy intersection calculation enables a more robust CODIS STR-DNA similarity calculation due to imprecision caused by noise produced by PCR machine. We also proposed shifted convoluted Gaussian fuzzy number (SCGFN) and Gaussian fuzzy number (GFN) to represent each locus value as improvement of triangular fuzzy number (TFN) as used in previous research. Compared to triangular fuzzy number (TFN), GFN is more realistic to represent uncertainty of locus information because the distribution is assumed to be Gaussian. Then, the original Gaussian fuzzy number (GFN) is convoluted with distribution of certain ethnic locus information to produce the new SCGFN which more represents ethnic information compared to original GFN. Experiments were done for the following cases: people with family relationships, people of the same tribe, and certain tribal populations. The statistical test with analysis of variance (ANOVA) shows the difference in similarity between SCGFN, GFN, and TFN with a significant level of 95%. The Tukey method in ANOVA shows that SCGFN yields a higher similarity which means being better than the GFN and TFN methods. The proposed method enables CODIS STR-DNA similarity calculation which is more robust to noise and performed better CODIS similarity calculation involving familial and tribal relationships.