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Hyperspectral imaging spectroscopy has facilitated the mapping of soil properties at large scales, but since the presence of photosynthetic or non-photosynthetic vegetation affects the reflectance spectra, soil properties mapping is limited to bare soil surfaces. This study analyzed the impact of bare soil pixel identification on clay content estimation using two methods (i) combination of two spectral indices, Normalized Difference Vegetation Index for identifying photosynthetic vegetation and Cellulose Absorption Index for non-photosynthetic vegetation and (ii) spectral unmixing for estimating fractions of soil, photosynthetic and non-photosynthetic vegetation. The study used AVIRIS-NG image and laboratory measured clay content of 272 soil samples acquired over Karnataka, India. Bare soil pixels were identified using the two methods and performances of partial least squares regression (PLSR) models used to estimate the clay contents and the predicted clay content maps were analyzed and compared. PLSR model based on bare soil pixels identified by unmixing provided slightly better performances (R2val of 0.61) than spectral indices (R2val of 0.46), even though the percentage of study area mapped was reduced by half. This study highlighted that an improvement in prediction performance comes at the cost of reduction in spatial coverage in mapping of clay content. [ABSTRACT FROM AUTHOR]

This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm optimization (EMPSO) technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called particles. In this study the standard particle swarm optimization (PSO) algorithm is further improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. The proposed approach is first tested on a hypothetical multireservoir system, used by earlier researchers. EMPSO showed promising results, when compared with other techniques. To show practical utility, EMPSO is then applied to a realistic case study, the Bhadra reservoir system in India, which serves multiple purposes, namely irrigation and hydropower generation. To handle multiple objectives of the problem, a weighted approach is adopted. The results obtained demonstrate that EMPSO is consistently performing better than the standard PSO and genetic algorithm techniques. It is seen that EMPSO is yielding better quality solutions with less number of function evaluations. DOI: 10.1061/(ASCE)0733-9496(2007)133:3(192) CE Database subject headings: Reservoir operation; Optimization; Irigation; Hydroelectric power generation.

Many water resources systems are characterized by multiple objectives. For multiobjective optimization, typically there can be no single optimal solution which can simultaneously satisfy all the goals, but rather a set of technologically efficient noninferior or Pareto optimal solutions exists. Generating those Pareto optimal solutions is a challenging task and often difficulties arise in using the conventional methods. In the optimization of reservoir systems, most of the times there is interdependence among one or more decision variables. Recently, it is emphasized that the evolutionary operators used in differential evolution algorithms are very much suitable for problems having interdependence among the decision variables. This paper utilizes this aspect and presents an efficient and effective approach for multiobjective optimization, namely multiobjective differential evolution (MODE) algorithm with an application to a case study in reservoir system optimization. The developed MODE algorithm is first tested on a few benchmark test problems and validated with standard performance measures by comparing them with the nondominated sorting genetic algorithm-II. On achieving satisfactory performance for test problems, it is applied to generate Pareto optimal solutions to a multiobjective reservoir operation problem. It is found that MODE provides many alternative Pareto optimal solutions with uniform coverage and convergence to true Pareto optimal fronts. The results obtained show that the proposed MODE can be a viable alternative for generating optimal trade-offs in multiobjective optimization of water resources systems. DOI: 10.1061/(ASCE)0887-3801 (2007)21:2(136) CE Database subject headings: Optimization; Algorithms; Reservoir operation; Irrigation; Hydroelectric power generation.