Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach-Reference-Cited by-同舟云学术

Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach

Author:

Aslam Bilal¹^ORCID,Maqsoom Ahsen²^ORCID,Cheema Ali Hassan²^ORCID,Ullah Fahim³^ORCID,Alharbi Abdullah⁴^ORCID,Imran Muhammad⁵^ORCID

Affiliation:

1. School of Informatics, Computing, Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

2. Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt, Islamabad, Pakistan

3. School of Surveying and Built Environment, University of Southern Queensland, Springfield Central, QLD, Australia

4. Department of Computer Science, Community College, King Saud University, Riyadh, Saudi Arabia

5. Institute of Innovation, Science and Sustainability, Federation University, Brisbane, QLD, Australia

Funder

King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference86 articles.

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

3. A Proficient Prediction Mechanism for Analyzing Water Quality Using Machine Learning Algorithms;2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS);2023-10-18

4. An Artificial Intelligence (AI) based Water Quality Index Detection and Ground Water Potential;2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS);2023-08-23