IEEE BASE PAPER TITLE:
Water Quality Classification Using SVM And XGBoost Method
OUR PROPOSED TITLE:
Water Quality Classification Using Machine Learning
IEEE BASE PAPER ABSTRACT:
Various pollutants have been endangering water quality over the past decades. As a result, predicting and modeling water quality have become essential to minimizing water pollution. This research has developed a classification algorithm to predict the water quality classification (WQC). The WQC is classified based on the water quality index (WQI) from 7 parameters in a dataset using Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). The results from the proposed model can accurately classify the water quality based on their features. The research outcome demonstrated that the XGBoost model performed better, with an accuracy of 94%, compared to the SVM model, with only a 67% accuracy. Even better, the XGBoost resulted in only 6% misclassification error compared to SVM, which had 33%. On top of that, XGBoost also obtained consistent superior results from 5-fold validation with an average accuracy of 90%, while SVM with an average accuracy of 64%. Considering the enhanced performance, XGBoost is concluded to be better at water quality classification.
OUR PROPOSED 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. Real-time 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.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Gradient Boosting Classifier.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
Hasriq Izzuan Hasnol Yusri, A’zraa Afhzan Ab Rahim, Siti Lailatul Mohd Hassan, Ili Shairah Abdul Halim, Noor Ezan Abdullah, “Water Quality Classification Using SVM And XGBoost Method”, 2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC), IEEE Conference, 2022.
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