What are the best metaheuristic approaches for groundwater management?
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The best metaheuristic approaches for groundwater management include the use of hybrid evolutionary algorithms such as none dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO) . These algorithms have been shown to improve the performance of the multi-layer perceptron (MLP) learning process in estimating groundwater level fluctuations . The MLP-2NSGA-II-MOPSO model, which combines these metaheuristic algorithms, has been found to outperform other models in terms of accuracy and speed, with an RMSE of 0.073, R of 0.98, and MAE of 0.059 . This model incorporates MLP parameters that result in a robust model, overcoming the issues of low conversions and local minimums . The use of metaheuristic optimization algorithms, such as NSGA-II and MOPSO, has been shown to enhance the performance of MLP models in groundwater management .
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| Papers (5) | Insight |
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| The provided paper does not discuss metaheuristic approaches for groundwater management. It focuses on using time series and ensemble methods for multiclassification of groundwater quality. | |
| The provided paper does not mention any specific metaheuristic approaches for groundwater management. | |
23 May 2023 | The provided paper does not specifically mention the best metaheuristic approaches for groundwater management. |
| The provided paper does not mention any specific metaheuristic approaches for groundwater management. The paper focuses on the prioritization of groundwater blocks and the identification of factors affecting declining trends in seasonal groundwater. | |
| The provided paper does not discuss the best metaheuristic approaches for groundwater management. |
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