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An algorithm for locating the best location for adding observation wells to enhance water level monitoring
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| Type: | Resource | |
| Storage: | The size of this resource is 2.3 MB | |
| Created: | Dec 31, 2023 at 2:42 a.m. | |
| Last updated: | May 15, 2024 at 5:28 a.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 450 |
| Downloads: | 188 |
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Abstract
The procedural goal is to determine the number and locations of new observation wells to be added to an existing observation network. Phases 1a and 1b both run once for each specified Number Of Additional Wells (NOAW). NOAW values range from 1 to MNAW (The MNAW in this study is 12 wells). For each NOAW, a Simple Genetic Algorithm (SGA) identifies the optimal location(s) of added well(s) by maximizing the inverse of the mean sum of squared differences between the Most Accurate Interpolated Values (MAIV) and a newly kriged surface (the kriging weights change with additional well(s)).
This model uses SGA that includes selection, crossover, and mutation to find the optimum location(s) for NOAW well(s). The Genetic Algorithm (GA) is a search algorithm based on the mechanics of natural selection and natural genetics which is frequently used to solve nonlinear optimization problems. Each optimization uses a different NOAW, ranging from one through MNAW. Each optimization stops iterating when: (1) the best objective function value does not change during 1000 consecutive iterations, and (2) the best objective-function value is better than or equal to the objective function value for “NOAW-1”. To provide SGA with feasible options, around 1000 uniformly spaced candidate well locations are provided.
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