INDEX-BASED CLASSIFICATION

Abstract

One of the most valuable natural resources ever given
to humans is water. The ecosystem and human health
are directly impacted by the water quality. Water is
used for many different things, including drinking,
farming, and industrial uses. Over the years,
numerous pollutants have put water quality in danger.
Predicting and estimating water quality are now
crucial to reducing water pollution as a result. Realtime monitoring is unsuccessful because
conventionally, water quality is assessed using
expensive laboratory and statistical processes. Low
water quality calls for a more workable and
economical solution. The proposed system builds a
model that can forecast the water quality index and
water quality class by utilizing the advantages of
machine learning techniques. This proposed system is
to develop a novel approach for water quality
classification using Gradient Boosting Classifier. The
method includes the calculation of the Water Quality
Index, which is used as a measure of water quality.
The proposed approach achieves a high Train
Accuracy of 98% and Test Accuracy of 94%. The
approach uses various water quality parameters and
features such as pH, dissolved oxygen, temperature,
and electrical conductivity to classify water into
different categories. The model developed in this
study is capable of predicting the water quality as
Excellent, Good, Poor and Very Poor, which can be
used for real-time monitoring and management of
water quality. The results demonstrate the
effectiveness and accuracy of the proposed approach
in predicting water quality, highlighting the potential
of machine learning techniques for water quality
monitoring and management. The proposed approach
can be used in various applications such as water
treatment, environmental monitoring, and aquatic life
management.