Harnessing machine learning tools for water quality assessment in the Kebili shallow aquifers,Southwestern Tunisia

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外文标题：Harnessing machine learning tools for water quality assessment in the Kebili shallow aquifers,Southwestern Tunisia

外文摘要：An integrated method that implements multivari-ate statistical analysis and ML methods to evaluate ground-water quality of the shallow aquifers of the Djerid and Kebili district,Southern Tunisia,was adopted.An evaluation of their suitability for irrigation and/or drinking purposes is necessary.A comprehensive hydrochemical assessment of 52 samples with entropy weighted water quality index(EWQI)was also proposed.Eleven water parameters were calculated to ascertain the potential use of those resources in irrigation and drinking.Multivariate analysis showed two main components with Dim1(variance=62.3％)and Dim.2(variance=22％),due to the bicarbonate,dissolu-tion,and evaporation and the intrusion of drainage water.The evaluation of water quality has been carried out using EWQI model.The calculated EWQI for the Djerid and Kebili waters(i.e.,52 samples)varied between 7.5 and 152.62,indicating a range of 145.12.A mean of 79.12 was lower than the median(88.47).From the calculation of EWQI,only 14 samples are not suitable for irrigation because of their poor to extremely poor quality(26.92％).The bivariate plot showed high correlation for EWQI-TH(r=0.93),EWQI～SAR(r=0.87),indicating that water quality depended on those parameters.Different ML algo-rithms were successfully applied for the water quality clas-sification.Our results indicated high prediction accuracy(SVM＞LDA＞ANN＞kNN)and perfect classification for kNN,LDA and Naive Bayes.For the purposes of develop-ing the prediction models,the dataset was divided into two groups:training(80％)and testing(20％).To evaluate the models'performance,RMSE,MSE,MAE and R2 metrics were used.kNN(R2=0.9359,MAE=6.49,MSE=79.00)and LDA(accuracy=97.56％;kappa=96.21％)achieved high accuracy.Moreover,linear regression indicated high correlation for both training(R2=0.9727)and testing data(0.9890).This well confirmed the validity of LDA algorithm in predicting water quality.Cross validation showed a high accuracy(92.31％),high sensitivity(89.47％)and high speci-ficity(95％).These findings are fundamentally important for an integrated water resource management in a larger context of sustainable development of the Kebili district.

外文关键词：

Water-resources managementMultivariate analysisMachine learningKebili and Djerid shallow aquifersEWQIWater classification

作者：

Zohra Kraiem、Kamel Zouari、Rim Trabelsi

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作者单位：

Laboratory of Radio-Analyses and Environment,National Engineering School,University of Sfax,1173,3038 Sfax,Tunisia

关键词：

Water-resources management Multivariate analysis Machine learning Kebili and Djerid shallow aquifers EWQI Water classification

出版年：

2024

DOI：