Machine Learning Algorithms for Predicting the Water Quality Index
Author:
Hussein Enas E.1ORCID, Jat Baloch Muhammad Yousuf2ORCID, Nigar Anam3, Abualkhair Hussain F.4ORCID, Aldawood Faisal Khaled5, Tageldin Elsayed6ORCID
Affiliation:
1. National Water Research Center, Shubra El-Kheima 13411, Egypt 2. College of New Energy and Environment, Jilin University, Changchun 130021, China 3. School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China 4. Department of Mechanical Engineering, Collage of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia 5. Department of Mechanical Engineering, College of Engineering, University of Bisha, P.O. Box 001, Bisha 67714, Saudi Arabia 6. Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt
Abstract
Groundwater is one of the water resources used to preserve natural water sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related to industry and agriculture result in groundwater contamination. Therefore, investigating water quality is essential for drinking and irrigation purposes. In this work, the water quality index (WQI) was used to identify the suitability of water for drinking and irrigation. However, generating an accurate WQI requires much time, as errors may be made during the sub-index calculations. Hence, an artificial intelligence (AI) prediction model was built to reduce both time and errors. Eighty data samples were collected from Sakrand, a city in the province of Sindh, to investigate the area’s WQI. The classification learners were used with raw data samples and the normalized data to select the best classifier among the following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), and discrimination analysis (DA). These were included in the classification learner tool in MATLAB. The results revealed that SVM was the best raw and normalized data classifier. The prediction accuracy levels for the training data were 90.8% and 89.2% for the raw and normalized data, respectively. Meanwhile, the prediction accuracy levels for the testing data were 86.67 and 93.33% for the raw and normalized data, respectively.
Funder
Deanship of Scientific Research, Taif University
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
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