Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms
- https://doi.org/10.2991/hcis.k.211203.001How to use a DOI?
- River water; water quality prediction; WQI; NN
The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model’s feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality, with Variable Importance values of 74.78, 36.805, 81.494, 105.770, 105.166, and 130.173, respectively.
- © 2021 The Authors. Publishing services by Atlantis Press International B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Md. Mehedi Hassan AU - Md. Mahedi Hassan AU - Laboni Akter AU - Md. Mushfiqur Rahman AU - Sadika Zaman AU - Khan Md. Hasib AU - Nusrat Jahan AU - Raisun Nasa Smrity AU - Jerin Farhana AU - M. Raihan AU - Swarnali Mollick PY - 2021 DA - 2021/12/10 TI - Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms JO - Human-Centric Intelligent Systems SP - 86 EP - 97 VL - 1 IS - 3-4 SN - 2667-1336 UR - https://doi.org/10.2991/hcis.k.211203.001 DO - https://doi.org/10.2991/hcis.k.211203.001 ID - Hassan2021 ER -