Control of Channel Bed Morphology in Large-Scale River Networks using a Genetic Algorithm Export

@article{citeulike:2552894, abstract = {Alluvial channels undergo continuous morphological changes caused by relationships between entrained sediment, variable flows and movable boundaries. Excess changes that occur through sediment degradation and deposition, however, tend to threaten the stability of bridges, hydraulic control structures and underground utilities. These changes also reduce conveyance capacity of a channel and diminish reservoir benefits associated with hydropower generation, flood control, and water supply. This article outlines the development of an optimal control methodology for minimizing sediment aggradation and degradation, thus controlling channel bed morphology, in large-scale multi-reservoir river systems. The sedimentation control problem is solved by coupling the U.S. Army Corps of Engineer's HEC-6 sediment transport simulation model with an immune genetic algorithm. The simulation model is used to implicitly solve governing hydraulic and sediment constraints, while the genetic algorithm is used to solve the overall control problem. The method is demonstrated first through application to a hypothetical river network from the literature, for which a comparison between the genetic algorithm and alternative optimization technique is made. A second application to an existing hydraulic network illustrates the practical utility of the methodology as a decision-making tool for sedimentation control.}, author = {Nicklow, John W. and Ozkurt, Ozlen and Bringer, John A.}, citeulike-article-id = {2552894}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1023609806431}, day = {1}, doi = {10.1023/A:1023609806431}, journal = {Water Resources Management}, keywords = {genetic-algorithm, hydroinformatics, optimal-design, optimization, options-generation, reservoir-operation}, month = {April}, number = {2}, pages = {113--132}, posted-at = {2008-03-18 20:26:43}, priority = {2}, title = {Control of Channel Bed Morphology in Large-Scale River Networks using a Genetic Algorithm}, url = {http://dx.doi.org/10.1023/A:1023609806431}, volume = {17}, year = {2003} }

TY - JOUR ID - citeulike:2552894 L3 - citeulike-article-id:2552894 N2 - Alluvial channels undergo continuous morphological changes caused by relationships between entrained sediment, variable flows and movable boundaries. Excess changes that occur through sediment degradation and deposition, however, tend to threaten the stability of bridges, hydraulic control structures and underground utilities. These changes also reduce conveyance capacity of a channel and diminish reservoir benefits associated with hydropower generation, flood control, and water supply. This article outlines the development of an optimal control methodology for minimizing sediment aggradation and degradation, thus controlling channel bed morphology, in large-scale multi-reservoir river systems. The sedimentation control problem is solved by coupling the U.S. Army Corps of Engineer's HEC-6 sediment transport simulation model with an immune genetic algorithm. The simulation model is used to implicitly solve governing hydraulic and sediment constraints, while the genetic algorithm is used to solve the overall control problem. The method is demonstrated first through application to a hypothetical river network from the literature, for which a comparison between the genetic algorithm and alternative optimization technique is made. A second application to an existing hydraulic network illustrates the practical utility of the methodology as a decision-making tool for sedimentation control. IS - 2 JF - Water Resources Management EP - 132 TI - Control of Channel Bed Morphology in Large-Scale River Networks using a Genetic Algorithm VL - 17 SP - 113 KW - genetic-algorithm KW - hydroinformatics KW - optimal-design KW - optimization KW - options-generation KW - reservoir-operation AU - Nicklow, John AU - Ozkurt, Ozlen AU - Bringer, John PY - 2003/04/01/ UR - http://dx.doi.org/10.1023/A:1023609806431 ER -