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

Yıl 2023, Cilt: 23 Sayı: 2, 265 - 278, 10.05.2023

Mustafa Yurtsever Murat Emeç

https://doi.org/10.21121/eab.1252167

Öz

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.

Anahtar Kelimeler

Water Quality Index Machine Learning Classification

Kaynakça

  • Abed, B. S., Farhan, A. R., Ismail, A. H., & Al Aani, S. (2022). Water quality index toward a reliable assessment for water supply uses: a novel approach. International Journal of Environmental Science and Technology, 19(4), 2885-2898.
  • Abuzir, S. Y., & Abuzir, Y. S. (2022). Machine learning for water quality classification. Water Quality Research Journal, 57(3), 152-164.
  • Ahmed, U., Mumtaz, R., Anwar, H., Shah, A. A., Irfan, R., & García-Nieto, J. (2019). Efficient water quality prediction using supervised machine learning. Water, 11(11), 2210.
  • Aldhyani, T. H., Al-Yaari, M., Alkahtani, H., & Maashi, M. (2020). Water quality prediction using artificial intelligence algorithms. Applied Bionics and Biomechanics, 2020.
  • Azrour, M., Mabrouki, J., Fattah, G., Guezzaz, A., & Aziz, F. (2022). Machine learning algorithms for efficient water quality prediction. Modeling Earth Systems and Environment, 8(2), 2793-2801.
  • Brown, R. M., McClelland, N. I. Deininger R. A. and O’Connor, M. F. (1972). Water Quality Index-Crashing, the Psychological Barrier, Proc. 6th Annual Conference, Advances in Water Pollution Research, pp 787-794.
  • Chafloque, R., Rodriguez, C., Pomachagua, Y., & Hilario, M. (2021, September). Predictive Neural Networks Model for Detection of Water Quality for Human Consumption. In 2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 172-176). IEEE.
  • Dilmi, S., & Ladjal, M. (2021). A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques. Chemometrics and Intelligent Laboratory Systems, 214, 104329.
  • Fen, L., Lei, Z., & Ting, C. (2021, November). Study on Potability Water Quality Classification Based on Integrated Learning. In 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (pp. 134-137). IEEE.
  • Graf, R., Zeldovich, M., & Friedrich, S. (2022). Comparing linear discriminant analysis and supervised learning algorithms for binary classification—A method comparison study. Biometrical Journal.
  • Kaddoura, S. (2022). Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability. Sustainability, 14(18), 11478.
  • Kadiwal, A. (2022) Water Quality [Dataset]. https://www.kaggle.com/adityakadiwal/water-potability. Accessed on 24 December 2022
  • Kaushik, P., Gupta, A., Roy, P. P., & Dogra, D. P. (2019). EEG-based age and gender prediction using deep BLSTM-LSTM network model. IEEE Sensors Journal, 19(7), 2634-2641.
  • Liou, S. M., Lo, S. L., & Wang, S. H. (2004). A generalized water quality index for Taiwan. Environmental monitoring and assessment, 96, 35-52.
  • Patel, J., Amipara, C., Ahanger, T. A., Ladhva, K., Gupta, R. K., Alsaab, H. O., ... & Ratna, R. (2022). A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI. Computational Intelligence and Neuroscience: CIN, 2022.
  • Rani, P., Kumar, R., & Jain, A. (2021). HIOC: a hybrid imputation method to predict missing values in medical datasets. International Journal of Intelligent Computing and Cybernetics, 14(4), 598-616.
  • National Geographic (2022) Rivers and streams. https://education.nationalgeographic.org/resource/resource-library-rivers-and-streams. Accessed 27 December 2022
  • Vapnik, VN (1998) Statistical learning theory. Adaptive and learning systems for signal processing. Communications and Control 2:1-740
  • Wang, X., Fu, L., & He, C. (2011). Applying support vector regression to water quality modelling by remote sensing data. International journal of remote sensing, 32(23), 8615-8627.
  • Wang, Y., Yuan, Y., Pan, Y., & Fan, Z. (2020). Modeling daily and monthly water quality indicators in a canal using a hybrid wavelet-based support vector regression structure. Water, 12(5), 1476.
  • WHO, UNICEF, World Bank (2022) State of the world’s drinking water: an urgent call to action to accelerate progress on ensuring safe drinking water for all. https://www.who.int/publications/i/item/9789240060807. Accessed 02 January 2023
  • Xie, G., Zhao, Y., Xie, S., Huang, M., & Zhang, Y. (2019). Multi-classification method for determining coastal water quality based on SVM with grid search and KNN. International Journal of Performability Engineering, 15(10), 2618.
  • Xin, L., & Mou, T. (2022). Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification. Wireless Communications and Mobile Computing, 2022.
  • Zhang, X., Yan, C., Gao, C., Malin, B. A., & Chen, Y. (2020). Predicting missing values in medical data via XGBoost regression. Journal of healthcare informatics research, 4, 383-394.
  • Zhu, M., Wang, J., Yang, X., Zhang, Y., Zhang, L., Ren, H., ... & Ye, L. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment & Health, 1(2), 107:116.

Yıl 2023, Cilt: 23 Sayı: 2, 265 - 278, 10.05.2023

Mustafa Yurtsever Murat Emeç

https://doi.org/10.21121/eab.1252167

Öz

Anahtar Kelimeler

Potable Water Quality Prediction Using Artificial Intelligence Water Quality Index Classification

Kaynakça

  • Abed, B. S., Farhan, A. R., Ismail, A. H., & Al Aani, S. (2022). Water quality index toward a reliable assessment for water supply uses: a novel approach. International Journal of Environmental Science and Technology, 19(4), 2885-2898.
  • Abuzir, S. Y., & Abuzir, Y. S. (2022). Machine learning for water quality classification. Water Quality Research Journal, 57(3), 152-164.
  • Ahmed, U., Mumtaz, R., Anwar, H., Shah, A. A., Irfan, R., & García-Nieto, J. (2019). Efficient water quality prediction using supervised machine learning. Water, 11(11), 2210.
  • Aldhyani, T. H., Al-Yaari, M., Alkahtani, H., & Maashi, M. (2020). Water quality prediction using artificial intelligence algorithms. Applied Bionics and Biomechanics, 2020.
  • Azrour, M., Mabrouki, J., Fattah, G., Guezzaz, A., & Aziz, F. (2022). Machine learning algorithms for efficient water quality prediction. Modeling Earth Systems and Environment, 8(2), 2793-2801.
  • Brown, R. M., McClelland, N. I. Deininger R. A. and O’Connor, M. F. (1972). Water Quality Index-Crashing, the Psychological Barrier, Proc. 6th Annual Conference, Advances in Water Pollution Research, pp 787-794.
  • Chafloque, R., Rodriguez, C., Pomachagua, Y., & Hilario, M. (2021, September). Predictive Neural Networks Model for Detection of Water Quality for Human Consumption. In 2021 13th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 172-176). IEEE.
  • Dilmi, S., & Ladjal, M. (2021). A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques. Chemometrics and Intelligent Laboratory Systems, 214, 104329.
  • Fen, L., Lei, Z., & Ting, C. (2021, November). Study on Potability Water Quality Classification Based on Integrated Learning. In 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (pp. 134-137). IEEE.
  • Graf, R., Zeldovich, M., & Friedrich, S. (2022). Comparing linear discriminant analysis and supervised learning algorithms for binary classification—A method comparison study. Biometrical Journal.
  • Kaddoura, S. (2022). Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability. Sustainability, 14(18), 11478.
  • Kadiwal, A. (2022) Water Quality [Dataset]. https://www.kaggle.com/adityakadiwal/water-potability. Accessed on 24 December 2022
  • Kaushik, P., Gupta, A., Roy, P. P., & Dogra, D. P. (2019). EEG-based age and gender prediction using deep BLSTM-LSTM network model. IEEE Sensors Journal, 19(7), 2634-2641.
  • Liou, S. M., Lo, S. L., & Wang, S. H. (2004). A generalized water quality index for Taiwan. Environmental monitoring and assessment, 96, 35-52.
  • Patel, J., Amipara, C., Ahanger, T. A., Ladhva, K., Gupta, R. K., Alsaab, H. O., ... & Ratna, R. (2022). A Machine Learning-Based Water Potability Prediction Model by Using Synthetic Minority Oversampling Technique and Explainable AI. Computational Intelligence and Neuroscience: CIN, 2022.
  • Rani, P., Kumar, R., & Jain, A. (2021). HIOC: a hybrid imputation method to predict missing values in medical datasets. International Journal of Intelligent Computing and Cybernetics, 14(4), 598-616.
  • National Geographic (2022) Rivers and streams. https://education.nationalgeographic.org/resource/resource-library-rivers-and-streams. Accessed 27 December 2022
  • Vapnik, VN (1998) Statistical learning theory. Adaptive and learning systems for signal processing. Communications and Control 2:1-740
  • Wang, X., Fu, L., & He, C. (2011). Applying support vector regression to water quality modelling by remote sensing data. International journal of remote sensing, 32(23), 8615-8627.
  • Wang, Y., Yuan, Y., Pan, Y., & Fan, Z. (2020). Modeling daily and monthly water quality indicators in a canal using a hybrid wavelet-based support vector regression structure. Water, 12(5), 1476.
  • WHO, UNICEF, World Bank (2022) State of the world’s drinking water: an urgent call to action to accelerate progress on ensuring safe drinking water for all. https://www.who.int/publications/i/item/9789240060807. Accessed 02 January 2023
  • Xie, G., Zhao, Y., Xie, S., Huang, M., & Zhang, Y. (2019). Multi-classification method for determining coastal water quality based on SVM with grid search and KNN. International Journal of Performability Engineering, 15(10), 2618.
  • Xin, L., & Mou, T. (2022). Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification. Wireless Communications and Mobile Computing, 2022.
  • Zhang, X., Yan, C., Gao, C., Malin, B. A., & Chen, Y. (2020). Predicting missing values in medical data via XGBoost regression. Journal of healthcare informatics research, 4, 383-394.
  • Zhu, M., Wang, J., Yang, X., Zhang, Y., Zhang, L., Ren, H., ... & Ye, L. (2022). A review of the application of machine learning in water quality evaluation. Eco-Environment & Health, 1(2), 107:116.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Mustafa Yurtsever 0000-0003-2232-0542

Murat Emeç 0000-0002-9407-1728

Erken Görünüm Tarihi 3 Mayıs 2023
Yayımlanma Tarihi 10 Mayıs 2023
Kabul Tarihi 1 Mart 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 23 Sayı: 2

Kaynak Göster

APA Yurtsever, M., & Emeç, M. (2023). Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability. Ege Academic Review, 23(2), 265-278. https://doi.org/10.21121/eab.1252167
AMA Yurtsever M, Emeç M. Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability. eab. Mayıs 2023;23(2):265-278. doi:10.21121/eab.1252167
Chicago Yurtsever, Mustafa, ve Murat Emeç. “Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability”. Ege Academic Review 23, sy. 2 (Mayıs 2023): 265-78. https://doi.org/10.21121/eab.1252167.
EndNote Yurtsever M, Emeç M (01 Mayıs 2023) Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability. Ege Academic Review 23 2 265–278.
IEEE M. Yurtsever ve M. Emeç, “Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability”, eab, c. 23, sy. 2, ss. 265–278, 2023, doi: 10.21121/eab.1252167.
ISNAD Yurtsever, Mustafa - Emeç, Murat. “Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability”. Ege Academic Review 23/2 (Mayıs 2023), 265-278. https://doi.org/10.21121/eab.1252167.
JAMA Yurtsever M, Emeç M. Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability. eab. 2023;23:265–278.
MLA Yurtsever, Mustafa ve Murat Emeç. “Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability”. Ege Academic Review, c. 23, sy. 2, 2023, ss. 265-78, doi:10.21121/eab.1252167.
Vancouver Yurtsever M, Emeç M. Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability. eab. 2023;23(2):265-78.
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