在硝酸盐浓度分析中使用关联规则挖掘技术和随机森林分割基尼指数算法加强地下水质量评估

Siddthan R, Shanthi Pm
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引用次数: 0

摘要

人类活动和不断变化的天气模式导致对地下水资源的需求不断增长。然而,评估地下水的质量至关重要。硝酸盐是一种重要的水污染物,可导致蓝婴综合症或高铁血红蛋白血症。因此,有必要评估地下水中的硝酸盐含量。目前的方法包括评估地下水质量并将其纳入模型。不适当的数据集、缺乏性能和其他限制因素是当前方法的局限性。使用地下水数据集并对数据进行预处理。对选定的数据进行特征提取,并与规则排序相关联。在建议的模型中,使用关联规则挖掘技术来应对这些挑战并评估地下水中的硝酸盐含量。规则排序方法采用关联规则挖掘技术来划分数据集。在拟议的数据分类模型中引入了分割基尼指数算法。分裂基尼指数算法是一种决策树归纳算法,用于为分类任务构建决策树。它基于基尼杂质度量,该度量可衡量数据集的异质性。使用 Naïve Bayes、SVM 和 KNN 算法对地下水质量进行了分类。通过计算精确度、准确度、F1 分数和召回值等性能指标,评估了所建议方法的效率。当前研究中建议的方法提高了 0.99 的准确率,表现出更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis
Human actions and changing weather patterns are contributing to the growing demand for groundwater resources. Nevertheless, evaluating the quality of groundwater is crucial. Nitrate is a significant water contaminant that can lead to blue-baby syndrome or methemoglobinemia. Therefore, it is necessary to assess the level of nitrate in groundwater. Current methods involve evaluating the quality of groundwater and integrating it into the models. The inappropriate datasets, lack of performance, and other constraints are limitations of current methods. Ground water dataset is used and pre-processed the data’s. Selected data’s are feature extracted and associated with the rule ranking. In the suggested model, the use of associative rule mining technique has been implemented to address these challenges and assess nitrate levels in groundwater. The method of rule ranking is carried out using association rule mining technique to divide the datasets. The split gini indexing algorithm is introduced in the proposed model for data classification. The Split Gini Indexing algorithm is a decision tree induction algorithm that is used to build decision trees for classification tasks. It is based on the Gini impurity measure, which measures the heterogeneity of a dataset. The quality of groundwater has been classified using Naïve Bayes, SVM, and KNN algorithms. The proposed approach's efficiency is evaluated by calculating performance metrics such as precision, accuracy, F1-score, and recall values. The suggested method in the current research attains an improved accuracy of 0.99, demonstrating enhanced performance.
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