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En el instante 21 de octubre de 2025, 9:00:56 UTC,
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Añadido recurso Roughness Parameter Estimation for flood numerical simulation using Differential Evolution a Roughness Parameter Estimation for flood numerical simulation using Differential Evolution
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| 2 | "author": "H Esqueda, SI Valdez, S Botello", | 2 | "author": "H Esqueda, SI Valdez, S Botello", | ||
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| 61 | "notes": "A methodology to estimate parameters necessary to carry | 61 | "notes": "A methodology to estimate parameters necessary to carry | ||
| 62 | out numerical simulations of flood phenomena is presented, that may be | 62 | out numerical simulations of flood phenomena is presented, that may be | ||
| 63 | useful for detecting flood-prone areas. Geospatial information | 63 | useful for detecting flood-prone areas. Geospatial information | ||
| 64 | contained in different databases is used as inputs, numerical | 64 | contained in different databases is used as inputs, numerical | ||
| 65 | simulation tools of hydrodynamic flooding phenomenon by solving the | 65 | simulation tools of hydrodynamic flooding phenomenon by solving the | ||
| 66 | shallow water equations, and stochastic optimization algorithms. The | 66 | shallow water equations, and stochastic optimization algorithms. The | ||
| 67 | objective is to find certain parameters of the simulation model that | 67 | objective is to find certain parameters of the simulation model that | ||
| 68 | have a high uncertainty degree through evolutionary algorithms, | 68 | have a high uncertainty degree through evolutionary algorithms, | ||
| 69 | comparing the simulations carried out with satellite images that | 69 | comparing the simulations carried out with satellite images that | ||
| 70 | monitor the behavior of rivers and streams in areas that may be | 70 | monitor the behavior of rivers and streams in areas that may be | ||
| 71 | susceptible to flooding. In this work, the Manning roughness | 71 | susceptible to flooding. In this work, the Manning roughness | ||
| 72 | coefficients were determined according to the soil usage identified in | 72 | coefficients were determined according to the soil usage identified in | ||
| 73 | a synthetic example, in order to evaluate the usefulness and viability | 73 | a synthetic example, in order to evaluate the usefulness and viability | ||
| 74 | of the methodology proposed.", | 74 | of the methodology proposed.", | ||
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| 101 | to carry out numerical simulations of flood phenomena is presented, | ||||
| 102 | that may be useful for detecting flood-prone areas. Geospatial | ||||
| 103 | information contained in different databases is used as inputs, | ||||
| 104 | numerical simulation tools of hydrodynamic flooding phenomenon by | ||||
| 105 | solving the shallow water equations, and stochastic optimization | ||||
| 106 | algorithms. The objective is to find certain parameters of the | ||||
| 107 | simulation model that have a high uncertainty degree through | ||||
| 108 | evolutionary algorithms, comparing the simulations carried out with | ||||
| 109 | satellite images that monitor the behavior of rivers and streams in | ||||
| 110 | areas that may be susceptible to flooding. In this work, the Manning | ||||
| 111 | roughness coefficients were determined according to the soil usage | ||||
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