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首页> 外文期刊>Journal of Hydrology >Optimization of drastic method by supervised committee machine artificial intelligence to assess groundwater vulnerability for maragheh-bonab plain aquifer, Iran
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Optimization of drastic method by supervised committee machine artificial intelligence to assess groundwater vulnerability for maragheh-bonab plain aquifer, Iran

机译：监督委员会机器人工智能对剧烈方法的优化，以评估伊朗马拉格-博纳布平原含水层的地下水脆弱性

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摘要

Contamination of wells with nitrate-N (NO_3-N) poses various threats to human health. Contamination of groundwater is a complex process and full of uncertainty in regional scale. Development of an integrative vulnerability assessment methodology can be useful to effectively manage (including prioritization of limited resource allocation to monitor high risk areas) and protect this valuable freshwater source. This study introduces a supervised committee machine with artificial intelligence (SCMAI) model to improve the DRASTIC method for groundwater vulnerability assessment for the Maragheh-Bonab plain aquifer in Iran. Four different AI models are considered in the SCMAI model, whose input is the DRASTIC parameters. The SCMAI model improves the committee machine artificial intelligence (CMAI) model by replacing the linear combination in the CMAI with a nonlinear supervised ANN framework. To calibrate the AI models, NO_3-N concentration data are divided in two datasets for the training and validation purposes. The target value of the AI models in the training step is the corrected vulnerability indices that relate to the first NO_3-N concentration dataset. After model training, the AI models are verified by the second NO_3-N concentration dataset. The results show that the four AI models are able to improve the DRASTIC method. Since the best AI model performance is not dominant, the SCMAI model is considered to combine the advantages of individual AI models to achieve the optimal performance. The SCMAI method re-predicts the groundwater vulnerability based on the different AI model prediction values. The results show that the SCMAI outperforms individual AI models and committee machine with artificial intelligence (CMAI) model. The SCMAI model ensures that no water well with high NO_3-N levels would be classified as low risk and vice versa. The study concludes that the SCMAI model is an effective model to improve the DRASTIC model and provides a confident estimate of the pollution risk
机译：硝酸盐氮（NO_3-N）污染井对人类健康构成各种威胁。地下水污染是一个复杂的过程，区域范围充满不确定性。综合脆弱性评估方法的开发对于有效管理（包括优先分配有限的资源以监视高风险区域）和保护这种宝贵的淡水水源非常有用。这项研究介绍了一种带人工智能的监督委员会机器（SCMAI）模型，以改进DRASTIC方法用于伊朗Maragheh-Bonab平原含水层的地下水脆弱性评估。在SCMAI模型中考虑了四个不同的AI模型，其输入是DRASTIC参数。 SCMAI模型通过用非线性监督的ANN框架替换CMAI中的线性组合，改进了委员会机器人工智能（CMAI）模型。为了校准AI模型，将NO_3-N浓度数据分为两个数据集以进行训练和验证。训练步骤中AI模型的目标值是与第一个NO_3-N浓度数据集有关的校正后的脆弱性指数。在模型训练之后，通过第二个NO_3-N浓度数据集验证AI模型。结果表明，四种AI模型都能够改进DRASTIC方法。由于最佳AI模型的性能并不占主导地位，因此SCMAI模型被认为结合了各个AI模型的优势来实现最佳性能。 SCMAI方法根据不同的AI模型预测值重新预测地下水脆弱性。结果表明，SCMAI优于单独的AI模型和具有人工智能（CMAI）模型的委员会机器。 SCMAI模型确保没有NO_3-N高水平的水井被归类为低风险，反之亦然。研究得出结论，SCMAI模型是改进DRASTIC模型的有效模型，并为污染风险提供了可靠的估计

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