Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability

Mustafa Yurtsever; Murat Emeç

Makale detayları

Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability

Yazarlar:

Mustafa Yurtsever | Murat Emeç

Yayıncı:

Ege University

Ana Başlıklar:

İşletme

Dergi:

Ege Akademik Bakış Dergisi Vol.23 Issue 2, pp.265-278

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285072

pISSN:

1303-099X

Anahtar kelimeler:

Özet:

 Water is one of the most important resources for human life and health. Global climate change, industrialization and urbanization pose serious dangers to existing water resources. Water quality has traditionally been predicted by expensive, time-consuming laboratory and statistical analysis. However, machine learning algorithms can be applied to determine the water quality index in real time efficiently and quickly. With this motivation, a dataset obtained from the Kaggle website was used to classify water quality in this research. Some features were found to be empty in the data set. Traditional methods drop, mean imputation and regression method were applied for null values. After the null values were completed, RF, Adaboost and XGBoost were applied for binary classification. Gridsearch and Randomsearch methods have been applied in hyper parameter optimization. Among all the algorithms used, the SXH hybrid method created with the Support Vector Regression SVR and XGBoost methods showed the best classification performance with 99.4 accuracy and F1-score. Comparison of our results with previous similar studies showed that our SVR XGboost Hybrid SXH model had the best performance ratio Accuracy, F1-score. The performance of our proposed model is proof that hybrid machine learning methods can provide an innovative perspective on potable water quality.

 Water is one of the most important resources for human life and health. Global climate change, industrialization and urbanization pose serious dangers to existing water resources. Water quality has traditionally been predicted by expensive, time-consuming laboratory and statistical analysis. However, machine learning algorithms can be applied to determine the water quality index in real time efficiently and quickly. With this motivation, a dataset obtained from the Kaggle website was used to classify water quality in this research. Some features were found to be empty in the data set. Traditional methods drop, mean imputation and regression method were applied for null values. After the null values were completed, RF, Adaboost and XGBoost were applied for binary classification. Gridsearch and Randomsearch methods have been applied in hyper parameter optimization. Among all the algorithms used, the SXH hybrid method created with the Support Vector Regression SVR and XGBoost methods showed the best classification performance with 99.4 accuracy and F1-score. Comparison of our results with previous similar studies showed that our SVR XGboost Hybrid SXH model had the best performance ratio Accuracy, F1-score. The performance of our proposed model is proof that hybrid machine learning methods can provide an innovative perspective on potable water quality.

 Water is one of the most important resources for human life and health. Global climate change, industrialization and urbanization pose serious dangers to existing water resources. Water quality has traditionally been predicted by expensive, time-consuming laboratory and statistical analysis. However, machine learning algorithms can be applied to determine the water quality index in real time efficiently and quickly. With this motivation, a dataset obtained from the Kaggle website was used to classify water quality in this research. Some features were found to be empty in the data set. Traditional methods drop, mean imputation and regression method were applied for null values. After the null values were completed, RF, Adaboost and XGBoost were applied for binary classification. Gridsearch and Randomsearch methods have been applied in hyper parameter optimization. Among all the algorithms used, the SXH hybrid method created with the Support Vector Regression SVR and XGBoost methods showed the best classification performance with 99.4 accuracy and F1-score. Comparison of our results with previous similar studies showed that our SVR XGboost Hybrid SXH model had the best performance ratio Accuracy, F1-score. The performance of our proposed model is proof that hybrid machine learning methods can provide an innovative perspective on potable water quality.

 Water is one of the most important resources for human life and health. Global climate change, industrialization and urbanization pose serious dangers to existing water resources. Water quality has traditionally been predicted by expensive, time-consuming laboratory and statistical analysis. However, machine learning algorithms can be applied to determine the water quality index in real time efficiently and quickly. With this motivation, a dataset obtained from the Kaggle website was used to classify water quality in this research. Some features were found to be empty in the data set. Traditional methods drop, mean imputation and regression method were applied for null values. After the null values were completed, RF, Adaboost and XGBoost were applied for binary classification. Gridsearch and Randomsearch methods have been applied in hyper parameter optimization. Among all the algorithms used, the SXH hybrid method created with the Support Vector Regression SVR and XGBoost methods showed the best classification performance with 99.4 accuracy and F1-score. Comparison of our results with previous similar studies showed that our SVR XGboost Hybrid SXH model had the best performance ratio Accuracy, F1-score. The performance of our proposed model is proof that hybrid machine learning methods can provide an innovative perspective on potable water quality.

Makale detayları

Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability

Yazarlar:

Mustafa Yurtsever | Murat Emeç

Yayıncı:

Ege University

Ana Başlıklar:

İşletme

Dergi:

Ege Akademik Bakış Dergisi Vol.23 Issue 2, pp.265-278

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285072

pISSN:

1303-099X

Water Quality Index

Machine Learning

Classification

Potable Water Quality Prediction Using Artificial Intelligence

Water Quality Index

Classification

Sayı

Makale detayları

Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability

Yazarlar: Mustafa Yurtsever | Murat Emeç

Yayıncı: Ege University

Ana Başlıklar: İşletme

Dergi: Ege Akademik Bakış Dergisi Vol.23 Issue 2, pp.265-278

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001285072

pISSN: 1303-099X

Water Quality Index

Machine Learning

Classification

Potable Water Quality Prediction Using Artificial Intelligence

Water Quality Index

Classification

Sayı

Yazarlar:

Mustafa Yurtsever | Murat Emeç

Yayıncı:

Ege University

Ana Başlıklar:

İşletme

Dergi:

Ege Akademik Bakış Dergisi Vol.23 Issue 2, pp.265-278

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001285072

pISSN:

1303-099X