使用监督机器学习技术预测加沙沿海含水层的地下水质量指数

IF 1.6 Q3 WATER RESOURCES
A. Aish, H. A. Zaqoot, W. Sethar, Diana A. Aish
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引用次数: 0

摘要

本文研究了五种监督机器学习算法,包括支持向量机(SVM)、逻辑回归(LogR)、决策树(DT)、多感知器神经网络(MLP-NN)和K近邻(KNN),用于预测加沙地带沿海含水层的水质指数(WQI)和水质等级(WQC)的性能。从加沙地带沿海含水层共采集了2448份地下水样本,并测量了各种物理和化学参数,以根据重量计算WQI。使用五个误差度量来评估预测准确性。结果表明,MLP-NN在准确性方面优于其他模型,R值为0.9945–0.9948,而SVM为0.9897–0.9880,LogR为0.9784–0.9800,KNN为0.9464–0.9247,DT为0.9301–0.9064。SVM分类显示,78.32%的研究区域属于较差至不适宜的水质类别,而该地区北部的水质为良好至优良。TDS是WQI预测中最重要的参数,而和是最不重要的。MLP-NN和SVM是加沙海岸含水层WQI预测和分类最准确的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of groundwater quality index in the Gaza coastal aquifer using supervised machine learning techniques
This paper investigates the performance of five supervised machine learning algorithms, including support vector machine (SVM), logistic regression (LogR), decision tree (DT), multiple perceptron neural network (MLP-NN), and K-nearest neighbours (KNN) for predicting the water quality index (WQI) and water quality class (WQC) in the coastal aquifer of the Gaza Strip. A total of 2,448 samples of groundwater were collected from the coastal aquifer of the Gaza Strip, and various physical and chemical parameters were measured to calculate the WQI based on weight. The prediction accuracy was evaluated using five error measures. The results showed that MLP-NN outperformed other models in terms of accuracy with an R value of 0.9945–0.9948, compared with 0.9897–0.9880 for SVM, 0.9784–0.9800 for LogR, 0.9464–0.9247 for KNN, and 0.9301–0.9064 for DT. SVM classification showed that 78.32% of the study area fell under poor to unsuitable water categories, while the north part of the region had good to excellent water quality. TDS was the most important parameter in WQI predictions while and were the least important. MLP-NN and SVM were the most accurate models for the WQI prediction and classification in the Gaza coastal aquifer.
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来源期刊
CiteScore
2.30
自引率
6.20%
发文量
136
审稿时长
14 weeks
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