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首页> 外文期刊>The Science of the Total Environment >Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)
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Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)

机译：监督情报委员会机器（SICM）制定的地下水脆弱性指数

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摘要

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentratioa The three Al-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters.
机译：这项研究提出了一种监督智能委员会机（SICM）模型来评估含水层的地下水脆弱性指数。 SICM使用人工神经网络（ANN）概括了三种人工智能（AI）模型：支持向量机（SVM），神经模糊（NF）和基因表达编程（GEP）。每个模型都使用DRASTIC索引，DRASTIC索引是7个地质，水文和水文地质参数的首字母缩写，共同代表固有（或自然）脆弱性，并赋予污染物诸如硝酸盐-N的渗透力，这些污染物会穿透地表的含水层。这些模型经过训练，可以通过测量硝酸盐-N浓度来修改或调节其DRASTIC指数值。三种铝技术通常具有相似的性能，但也存在差异，因此SICM利用这种情况通过选择混合的建模结果来改善建模值性能更好的SVM，NF和GEP组件。 Ardabil含水层研究区域的模型显示，DRASTIC框架的脆弱性指数产生了锋利的前沿，但是AI模型使前沿变得平滑，并反映了与观测到的硝酸盐值更好的相关性。 SICM改进了三种AI模型的性能，并很好地应对了异构性和不确定性参数。

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