Machine Learning Algorithms for Predicting the Water Quality Index DOI Open Access
Enas E. Hussein, Muhammad Yousuf Jat Baloch, Anam Nigar

et al.

Water, Journal Year: 2023, Volume and Issue: 15(20), P. 3540 - 3540

Published: Oct. 11, 2023

Groundwater is one of the water resources used to preserve natural sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related industry agriculture result groundwater contamination. Therefore, investigating quality essential drinking irrigation purposes. In this work, index (WQI) was identify suitability irrigation. However, generating an accurate WQI requires much time, as errors may be made during sub-index calculations. Hence, artificial intelligence (AI) prediction model built reduce both time errors. Eighty data samples were collected from Sakrand, a city province Sindh, investigate area’s WQI. The classification learners with raw normalized select best classifier among following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), discrimination analysis (DA). These included learner tool MATLAB. results revealed that SVM classifier. accuracy levels training 90.8% 89.2% data, respectively. Meanwhile, testing 86.67 93.33%

Language: Английский

Integrated Approach to Hydrogeochemical Assessment of Groundwater Quality in Major Industrial Zone of Punjab, Pakistan DOI
Asmat Ali, Zahid Ullah,

Nayab Ismaeel

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(23), P. 34396 - 34414

Published: May 4, 2024

Language: Английский

Citations

5
Machine Learning Algorithms for Predicting the Water Quality Index DOI Open Access
Enas E. Hussein, Muhammad Yousuf Jat Baloch, Anam Nigar

et al.

Water, Journal Year: 2023, Volume and Issue: 15(20), P. 3540 - 3540

Published: Oct. 11, 2023

Groundwater is one of the water resources used to preserve natural sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related industry agriculture result groundwater contamination. Therefore, investigating quality essential drinking irrigation purposes. In this work, index (WQI) was identify suitability irrigation. However, generating an accurate WQI requires much time, as errors may be made during sub-index calculations. Hence, artificial intelligence (AI) prediction model built reduce both time errors. Eighty data samples were collected from Sakrand, a city province Sindh, investigate area’s WQI. The classification learners with raw normalized select best classifier among following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), discrimination analysis (DA). These included learner tool MATLAB. results revealed that SVM classifier. accuracy levels training 90.8% 89.2% data, respectively. Meanwhile, testing 86.67 93.33%

Language: Английский

Citations

8