Data Driven Explainable Predictive Features f...

Data Driven Explainable Predictive Features for Water Quality Prediction using Ensemble Learning

Author Name : Prof. N. Sendhil Kumar , V. Haritha, S. Syamala , D. Afroz Begum

ABSTRACT The worsening condition of lakes, streams, and estuaries is a severe and concerning issue. challenges encountered by the human race. The consequences of using untreated water are extensive, affecting various aspects of life. As a result, the administration of Water resources play a vital role in enhancing the overall quality of water. The issue of water contamination can be addressed and resolved. Efficiency can be achieved if data is analyzed and future water quality is predicted in advance. Throughout numerous past instances, this matter has been dealt with. Research has been conducted, but there is still a need for further improvement in the effectiveness, reliability, accuracy, and usability of the existing methods. Methods for managing the quality of water. this research aims to create a model for predicting water quality using the assistance of. The assessment of the precision of the implemented models as per the stated error metrics revealed that ensemble learning was the most accurate. Upon analyzing the outcomes of the models, it was found that each of them displayed certain tendencies to overestimate.