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The Brahmaputra River (BR) is a heavily braided river, due to various intricate paths, high discharge variability and bank erodibility, as well as multi-channel features, which, in turn, cause huge energy dissipation. The river also experiences anastomosing planform changes in response to seasonal water and sediment waves, resulting in a morphology with extreme complexity. The purpose of this study was to provide detailed and quantitative insights into the properties of planform complexity and dynamics of channel patterns that can complement previous studies. This was achieved by investigating the applicability of the anastomosing classification on the Brahmaputra river’s planform, and computing disorder/unpredictability and complexity of fluctuations using the notion of entropy and uniformity of energy conversion rate by the channels, by means of a power spectral density approach. In addition, we also evaluated their correlation with discharge as a dynamic imprint of river systems on alluvial landscapes, in order to test the hypothesis that river flow may be responsible for the development of anastomosing planforms. The analysis suggests that higher discharge values could lead to less complex planform and less fluctuations on the alluvial landscape, as compared to lower discharge values. The proposed framework has significant potential to assist in understanding the response of complex alluvial planform under flow dynamics for the BR and other similar systems.

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Dubey et al. [40] have shown that solving an Atanassov's I-fuzzy Linear Programming Problem represented by Atanassov's I-fuzzy sets with linear membership and non-membership functions is equivalent to solving an appropriate fuzzy optimisation problem with piecewise linear S-shaped membership functions. The equivalence is established using Hurwicz optimism-pessimism criterion [38] and indeterminacy resolution in Atanassov's I-fuzzy sets. Moreover, in case of convex break points in the piecewise linear membership function, the crisp counterpart of the equivalent optimisation problem involves binary variables. Here, in this paper we first convert the resulting fuzzy optimisation problem having convex break points into an equivalent fuzzy optimisation problem having concave break points on the lines of Inuiguchi et al. [34], before formulating its crisp equivalent. The advantage of this strategy is that the resulting crisp equivalent problem has no binary variables. Further, we also make use of the indeterminacy factor resolution principle to establish a duality relation which can be interpreted as a Atanassov's I-fuzzy variant of the (crisp) weak duality theorem.

The linguistic terms in a balanced linguistic term set describing qualitative data are symmetrical around the central linguistic word. With the growing complexity of the problems, the symmetric linguistic term set appears to be confined. This work examines the multiple criteria group decision-making problems where decision-makers employ a 2-tuple unbalanced linguistic term set to provide entries of alternativecriteria matrices. We adopt a data envelopment analysis (DEA) method and create a linear programming model to evaluate alternative-criteria weights for each decision-maker. The value function from prospect theory models the non-rational aspect of risk in criteria.The values of prospect gain and prospect loss on cost and benefit criteria are computed and used to create a DEA model that evaluates the weights of each criterion on each alternative. Finally, the entropy values of the cross-efficiency scores deliver a ranking of the alter-I. Khan (corresponding author)