Unplanned exploitation of groundwater constitutes emerging water-related threats to MayoTsanaga River Basin. Shallow groundwater from crystalline and detrital sediment aquifers, together with rain, dams, springs, and rivers were chemically and isotopically investigated to appraise its evolution, recharge source and mechanisms, flow direction, and age which were used to evaluate the groundwater susceptibility to contamination and the basin's stage of salinization. The groundwater which is Ca-Na-HCO 3 type is a chemically evolved equivalent of surface waters and rain water with Ca-Mg-Cl-SO 4 chemistry. The monsoon rain recharged the groundwater preferentially at an average rate of 74 mm/year, while surface waters recharge upon evaporation. Altitude effect of rain and springs show a similar variation of -0.4% for d 18 O/100 m, but the springs which were recharged at 452, 679, and 773 m asl show enrichment of d 18 O through evaporation by 0.8% corresponding to 3% of water loss during recharge. The groundwater which shows both local and regional flow regimes gets older towards the basins' margin with coeval enrichment in Fand depletion in NO 3 -. Incidentally, younger groundwaters are susceptible to anthropogenic contamination and older groundwaters are sinks of lithologenic fluoride. The basins salinization is still at an early stage.

This article proposes a multipopulation-based adaptive differential evolution (DE) algorithm to solve dynamic optimization problems (DOPs) in an efficient way. The algorithm uses Brownian and adaptive quantum individuals in conjunction with the DE individuals to maintain the diversity and exploration ability of the population. This algorithm, denoted as dynamic DE with Brownian and quantum individuals (DDEBQ), uses a neighborhood-driven double mutation strategy to control the perturbation and thereby prevents the algorithm from converging too quickly. In addition, an exclusion rule is used to spread the subpopulations over a larger portion of the search space as this enhances the optima tracking ability of the algorithm. Furthermore, an aging mechanism is incorporated to prevent the algorithm from stagnating at any local optimum. The performance of DDEBQ is compared with several state-of-the-art evolutionary algorithms using a suite of benchmarks from the generalized dynamic benchmark generator (GDBG) system used in the competition on evolutionary computation in dynamic and uncertain environments, held under the 2009 IEEE Congress on Evolutionary Computation (CEC). The simulation results indicate that DDEBQ outperforms other algorithms for most of the tested DOP instances in a statistically meaningful way.

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In this article, we introduce an improved optimization based technique for the synthesis of circular antenna array. The main objective is to achieve minimum side lobe levels, maximum directivity and null control for the non-uniform, planar circular antenna array. The design procedure utilizes an improved variant of a prominent and efficient metaheuristic algorithm of current interest, namely the Differential Evolution (DE). An efficient classical local search technique called Solis Wet's algorithm is incorporated with the competitive Differential Evolution. While the competitive DE is used for the global exploration, Solis Wet's algorithm is employed for local search.Combining the capability of both techniques the hybrid algorithm exhibits improved performance for circular array design problem. Three examples of circular array design problems are considered to illustrate the effectiveness of the hybrid algorithm cDESW (Competiteve Differential Evolution with Solis Wet's technique). The design results obtained using cDESW has comfortably outperformed the results obtained by other state-of-theart metaheuristics like CLPSO, JADE.