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Determining Water Quality in Urban Drinking Water Networks Using Artificial Intelligence Approaches

Yıl 2022, Cilt: 34 Sayı: 1, 49 - 57, 27.03.2022

Öz

Water has vital importance as it provides balance to all living things and keeps their activities alive. The quality of drinking water depends on the chemical, biological, physical and radiological criteria of water. Water quality is a factor that directly affects human health and ecological system. Many techniques and models have been developed to measure the quality of drinking water in urban networks. With the rapid increase in the world population, the number of drinking water networks in vital areas is also increasing. It is a time-consuming process for people to be able to respond to such needs instantly. Recent technological developments are playing an active role in solving such problems. In this study, artificial intelligence-based analyzes were performed on an open data set with water components. The analyzes performed determined whether the water was of good quality. Machine learning methods and ensemble learning algorithms were used in the experimental analysis. The best performance result was obtained using the bagging method. With this method, an overall accuracy of 96.44% was achieved.

Kaynakça

  • [1] A. Boretti, L. Rosa, Reassessing the projections of the World Water Development Report, Npj Clean Water. 2 (2019) 15. doi:10.1038/s41545-019-0039-9.
  • [2] R.J. Hogeboom, The Water Footprint Concept and Water’s Grand Environmental Challenges, One Earth. 2 (2020) 218–222. doi:https://doi.org/10.1016/j.oneear.2020.02.010.
  • [3] T.H.H. Aldhyani, M. Al-Yaari, H. Alkahtani, M. Maashi, Water Quality Prediction Using Artificial Intelligence Algorithms, Appl. Bionics Biomech. 2020 (2020) 6659314. doi:10.1155/2020/6659314.
  • [4] M. Allaire, H. Wu, U. Lall, National trends in drinking water quality violations, Proc. Natl. Acad. Sci. 115 (2018) 2078–2083. doi:10.1073/pnas.1719805115.
  • [5] Water | United Nations, UN Water. (2021). https://www.un.org/en/global-issues/water (accessed December 29, 2021).
  • [6] M.S. Islam Khan, N. Islam, J. Uddin, S. Islam, M.K. Nasir, Water quality prediction and classification based on principal component regression and gradient boosting classifier approach, J. King Saud Univ. - Comput. Inf. Sci. (2021). doi:https://doi.org/10.1016/j.jksuci.2021.06.003.
  • [7] N. Radhakrishnan, A.S. Pillai, Comparison of Water Quality Classification Models using Machine Learning, in: 2020 5th Int. Conf. Commun. Electron. Syst., 2020: pp. 1183–1188. doi:10.1109/ICCES48766.2020.9137903.
  • [8] D. Venkata Vara Prasad, L. Y Venkataramana, P.S. Kumar, G. Prasannamedha, K. Soumya, P. A.J., Water quality analysis in a lake using deep learning methodology: prediction and validation, Int. J. Environ. Anal. Chem. (2020) 1–16. doi:10.1080/03067319.2020.1801665.
  • [9] P. Smarty, Water Quality Dataset, Kaggle. (2021). https://www.kaggle.com/mssmartypants/water-quality (accessed December 31, 2021).
  • [10] P. Schober, T.R. Vetter, Logistic Regression in Medical Research, Anesth. Analg. 132 (2021) 365–366. doi:10.1213/ANE.0000000000005247.
  • [11] M. Toğaçar, B. Ergen, M.E. Sertkaya, Zatürre Hastalığının Derin Öğrenme Modeli ile Tespiti, Fırat Üniversitesi Mühendislik Bilim. Derg. 31 (2019) 223–230.
  • [12] M. Jawthari, V. Stoffová, Predicting students’ academic performance using a modified kNN algorithm, Pollack Period. 16 (2021) 20–26. doi:10.1556/606.2021.00374.
  • [13] M.E. Sertkaya, B. Ergen, M. Togacar, Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images, in: 2019 23rd Int. Conf. Electron., 2019: pp. 1–5. doi:10.1109/electronics.2019.8765579.
  • [14] A. Topîrceanu, G. Grosseck, Decision tree learning used for the classification of student archetypes in online courses, Procedia Comput. Sci. 112 (2017) 51–60. doi:https://doi.org/10.1016/j.procs.2017.08.021.
  • [15] H. Polat, M. Turkoglu, O. Polat, Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET, IET Commun. 14 (2020) 4089–4100. doi:10.1049/iet-com.2020.0477.
  • [16] V. Tümen, B. Ergen, Intersections and crosswalk detection using deep learning and image processing techniques, Phys. A Stat. Mech. Its Appl. 543 (2020) 123510. doi:10.1016/j.physa.2019.123510.
  • [17] M. Schonlau, R.Y. Zou, The random forest algorithm for statistical learning, Stata J. Promot. Commun. Stat. Stata. 20 (2020) 3–29. doi:10.1177/1536867X20909688.
  • [18] V. Chang, T. Li, Z. Zeng, Towards an improved Adaboost algorithmic method for computational financial analysis, J. Parallel Distrib. Comput. 134 (2019) 219–232. doi:10.1016/j.jpdc.2019.07.014.
  • [19] A. Ibrahem Ahmed Osman, A. Najah Ahmed, M.F. Chow, Y. Feng Huang, A. El-Shafie, Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia, Ain Shams Eng. J. (2021). doi:https://doi.org/10.1016/j.asej.2020.11.011.
  • [20] T. Carneiro, R.V.M. Da NóBrega, T. Nepomuceno, G. Bian, V.H.C. De Albuquerque, P.P.R. Filho, Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications, IEEE Access. 6 (2018) 61677–61685. doi:10.1109/access.2018.2874767.
  • [21] E. Başaran, Z. Cömert, A. Şengür, Ü. Budak, Y. Çelik, M. Toğaçar, Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network, in: 2019 4th Int. Conf. Comput. Sci. Eng., 2019: pp. 1–4. doi:10.1109/ubmk.2019.8907070.
  • [22] M. Toğaçar, B. Ergen, Deep Learning Approach for Classification of Breast Cancer, in: 2018 Int. Conf. Artif. Intell. Data Process., 2018: pp. 1–5. doi:10.1109/idap.2018.8620802.
  • [23] M. Toğaçar, B. Ergen, Biyomedikal Görüntülerde Derin Öğrenme ile Mevcut Yöntemlerin Kıyaslanması, Fırat Üniversitesi Mühendislik Bilim. Derg. 31 (2019) 109–121.
  • [24] K.-S. Cheng, J.-Y. Ling, T.-W. Lin, Y.-T. Liu, Y.-C. Shen, Y. Kono, Quantifying Uncertainty in Land-Use/Land-Cover Classification Accuracy: A Stochastic Simulation Approach, Front. Environ. Sci. 9 (2021) 46. doi:10.3389/fenvs.2021.628214.
  • [25] Scikit-learn developers, Machine learning, Scikit-Learn. (2019). https://scikit-learn.org/stable/supervised_learning.html#supervised-learning (accessed January 2, 2022).
  • [26] Scikit-learn developers, Ensemble methods, Scikit-Learn. (2019). https://scikit-learn.org/stable/modules/ensemble.html (accessed January 2, 2022).
  • [27] C. Sillberg, P. Kullavanijaya, O. Chavalparit, Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River, J. Ecol. Eng. 22 (2021) 70–86. doi:10.12911/22998993/141364.
  • [28] E.Q. Shahra, W. Wu, S. Basurra, S. Rizou, Deep Learning for Water Quality Classification in Water Distribution Networks, in: 2021: pp. 153–164. doi:10.1007/978-3-030-80568-5_13.
  • [29] M. Hmoud Al-Adhaileh, F. Waselallah Alsaade, Modelling and Prediction of Water Quality by Using Artificial Intelligence, Sustainability. 13 (2021) 4259. doi:10.3390/su13084259.

Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi

Yıl 2022, Cilt: 34 Sayı: 1, 49 - 57, 27.03.2022

Öz

Öz: Su, bütün canlıların yaşam dengesini sağlayan ve faaliyetlerini ayakta tutan hayati bir öneme sahiptir. İçme suyunun kalitesi suyun kimyasal, biyolojik, fiziksel ve radyolojik ölçütleriyle bağlantılıdır. Su kalitesi insan sağlığını ve ekolojik sistemi doğrudan etkileyen bir faktördür. Kentsel şebekelerde içme suyunun kalitesini ölçebilmek için birçok teknik ve model geliştirilmiştir. Dünya nüfusunun hızlı artışı ve yaşamsal alanlardaki içme suyu şebekelerinin sayısını da artırmaktadır. Bu tür ihtiyaçlara anlık cevap verebilmek insanlar için zaman alıcı bir süreçtir. Son zamanlardaki teknolojik gelişmeler bu tür problemlerin çözümünde etkin rol üstlenmektedir. Bu çalışmada su bileşenlerini içeren açık veri kümesi kullanılarak yapay zekâ tabanlı analizler gerçekleştirilmiştir. Gerçekleştirilen analizlerde suyun kaliteli olup olmadığı tespit edilmiştir. Deneysel analizlerde makine öğrenme yöntemleri ve topluluk öğrenme algoritmaları kullanılmıştır. En iyi performans sonucu torbalama yöntemi ile elde edilmiştir. Bu yöntem ile %96,44 oranında genel doğruluk başarısı sağlanmıştır.

Kaynakça

  • [1] A. Boretti, L. Rosa, Reassessing the projections of the World Water Development Report, Npj Clean Water. 2 (2019) 15. doi:10.1038/s41545-019-0039-9.
  • [2] R.J. Hogeboom, The Water Footprint Concept and Water’s Grand Environmental Challenges, One Earth. 2 (2020) 218–222. doi:https://doi.org/10.1016/j.oneear.2020.02.010.
  • [3] T.H.H. Aldhyani, M. Al-Yaari, H. Alkahtani, M. Maashi, Water Quality Prediction Using Artificial Intelligence Algorithms, Appl. Bionics Biomech. 2020 (2020) 6659314. doi:10.1155/2020/6659314.
  • [4] M. Allaire, H. Wu, U. Lall, National trends in drinking water quality violations, Proc. Natl. Acad. Sci. 115 (2018) 2078–2083. doi:10.1073/pnas.1719805115.
  • [5] Water | United Nations, UN Water. (2021). https://www.un.org/en/global-issues/water (accessed December 29, 2021).
  • [6] M.S. Islam Khan, N. Islam, J. Uddin, S. Islam, M.K. Nasir, Water quality prediction and classification based on principal component regression and gradient boosting classifier approach, J. King Saud Univ. - Comput. Inf. Sci. (2021). doi:https://doi.org/10.1016/j.jksuci.2021.06.003.
  • [7] N. Radhakrishnan, A.S. Pillai, Comparison of Water Quality Classification Models using Machine Learning, in: 2020 5th Int. Conf. Commun. Electron. Syst., 2020: pp. 1183–1188. doi:10.1109/ICCES48766.2020.9137903.
  • [8] D. Venkata Vara Prasad, L. Y Venkataramana, P.S. Kumar, G. Prasannamedha, K. Soumya, P. A.J., Water quality analysis in a lake using deep learning methodology: prediction and validation, Int. J. Environ. Anal. Chem. (2020) 1–16. doi:10.1080/03067319.2020.1801665.
  • [9] P. Smarty, Water Quality Dataset, Kaggle. (2021). https://www.kaggle.com/mssmartypants/water-quality (accessed December 31, 2021).
  • [10] P. Schober, T.R. Vetter, Logistic Regression in Medical Research, Anesth. Analg. 132 (2021) 365–366. doi:10.1213/ANE.0000000000005247.
  • [11] M. Toğaçar, B. Ergen, M.E. Sertkaya, Zatürre Hastalığının Derin Öğrenme Modeli ile Tespiti, Fırat Üniversitesi Mühendislik Bilim. Derg. 31 (2019) 223–230.
  • [12] M. Jawthari, V. Stoffová, Predicting students’ academic performance using a modified kNN algorithm, Pollack Period. 16 (2021) 20–26. doi:10.1556/606.2021.00374.
  • [13] M.E. Sertkaya, B. Ergen, M. Togacar, Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images, in: 2019 23rd Int. Conf. Electron., 2019: pp. 1–5. doi:10.1109/electronics.2019.8765579.
  • [14] A. Topîrceanu, G. Grosseck, Decision tree learning used for the classification of student archetypes in online courses, Procedia Comput. Sci. 112 (2017) 51–60. doi:https://doi.org/10.1016/j.procs.2017.08.021.
  • [15] H. Polat, M. Turkoglu, O. Polat, Deep network approach with stacked sparse autoencoders in detection of DDoS attacks on SDN‐based VANET, IET Commun. 14 (2020) 4089–4100. doi:10.1049/iet-com.2020.0477.
  • [16] V. Tümen, B. Ergen, Intersections and crosswalk detection using deep learning and image processing techniques, Phys. A Stat. Mech. Its Appl. 543 (2020) 123510. doi:10.1016/j.physa.2019.123510.
  • [17] M. Schonlau, R.Y. Zou, The random forest algorithm for statistical learning, Stata J. Promot. Commun. Stat. Stata. 20 (2020) 3–29. doi:10.1177/1536867X20909688.
  • [18] V. Chang, T. Li, Z. Zeng, Towards an improved Adaboost algorithmic method for computational financial analysis, J. Parallel Distrib. Comput. 134 (2019) 219–232. doi:10.1016/j.jpdc.2019.07.014.
  • [19] A. Ibrahem Ahmed Osman, A. Najah Ahmed, M.F. Chow, Y. Feng Huang, A. El-Shafie, Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia, Ain Shams Eng. J. (2021). doi:https://doi.org/10.1016/j.asej.2020.11.011.
  • [20] T. Carneiro, R.V.M. Da NóBrega, T. Nepomuceno, G. Bian, V.H.C. De Albuquerque, P.P.R. Filho, Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications, IEEE Access. 6 (2018) 61677–61685. doi:10.1109/access.2018.2874767.
  • [21] E. Başaran, Z. Cömert, A. Şengür, Ü. Budak, Y. Çelik, M. Toğaçar, Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network, in: 2019 4th Int. Conf. Comput. Sci. Eng., 2019: pp. 1–4. doi:10.1109/ubmk.2019.8907070.
  • [22] M. Toğaçar, B. Ergen, Deep Learning Approach for Classification of Breast Cancer, in: 2018 Int. Conf. Artif. Intell. Data Process., 2018: pp. 1–5. doi:10.1109/idap.2018.8620802.
  • [23] M. Toğaçar, B. Ergen, Biyomedikal Görüntülerde Derin Öğrenme ile Mevcut Yöntemlerin Kıyaslanması, Fırat Üniversitesi Mühendislik Bilim. Derg. 31 (2019) 109–121.
  • [24] K.-S. Cheng, J.-Y. Ling, T.-W. Lin, Y.-T. Liu, Y.-C. Shen, Y. Kono, Quantifying Uncertainty in Land-Use/Land-Cover Classification Accuracy: A Stochastic Simulation Approach, Front. Environ. Sci. 9 (2021) 46. doi:10.3389/fenvs.2021.628214.
  • [25] Scikit-learn developers, Machine learning, Scikit-Learn. (2019). https://scikit-learn.org/stable/supervised_learning.html#supervised-learning (accessed January 2, 2022).
  • [26] Scikit-learn developers, Ensemble methods, Scikit-Learn. (2019). https://scikit-learn.org/stable/modules/ensemble.html (accessed January 2, 2022).
  • [27] C. Sillberg, P. Kullavanijaya, O. Chavalparit, Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River, J. Ecol. Eng. 22 (2021) 70–86. doi:10.12911/22998993/141364.
  • [28] E.Q. Shahra, W. Wu, S. Basurra, S. Rizou, Deep Learning for Water Quality Classification in Water Distribution Networks, in: 2021: pp. 153–164. doi:10.1007/978-3-030-80568-5_13.
  • [29] M. Hmoud Al-Adhaileh, F. Waselallah Alsaade, Modelling and Prediction of Water Quality by Using Artificial Intelligence, Sustainability. 13 (2021) 4259. doi:10.3390/su13084259.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm FBD
Yazarlar

Abdullah Şener 0000-0002-8927-5638

Burhan Ergen 0000-0003-3244-2615

Hamit Mızrak 0000-0002-4795-3007

Yayımlanma Tarihi 27 Mart 2022
Gönderilme Tarihi 2 Ocak 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 34 Sayı: 1

Kaynak Göster

APA Şener, A., Ergen, B., & Mızrak, H. (2022). Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi. Fırat Üniversitesi Fen Bilimleri Dergisi, 34(1), 49-57.
AMA Şener A, Ergen B, Mızrak H. Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi. Fırat Üniversitesi Fen Bilimleri Dergisi. Mart 2022;34(1):49-57.
Chicago Şener, Abdullah, Burhan Ergen, ve Hamit Mızrak. “Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi”. Fırat Üniversitesi Fen Bilimleri Dergisi 34, sy. 1 (Mart 2022): 49-57.
EndNote Şener A, Ergen B, Mızrak H (01 Mart 2022) Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi. Fırat Üniversitesi Fen Bilimleri Dergisi 34 1 49–57.
IEEE A. Şener, B. Ergen, ve H. Mızrak, “Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi”, Fırat Üniversitesi Fen Bilimleri Dergisi, c. 34, sy. 1, ss. 49–57, 2022.
ISNAD Şener, Abdullah vd. “Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi”. Fırat Üniversitesi Fen Bilimleri Dergisi 34/1 (Mart 2022), 49-57.
JAMA Şener A, Ergen B, Mızrak H. Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi. Fırat Üniversitesi Fen Bilimleri Dergisi. 2022;34:49–57.
MLA Şener, Abdullah vd. “Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi”. Fırat Üniversitesi Fen Bilimleri Dergisi, c. 34, sy. 1, 2022, ss. 49-57.
Vancouver Şener A, Ergen B, Mızrak H. Kentsel İçme Suyu Şebekelerinde Suyun Kalite Seviyesinin Yapay Zekâ Yaklaşımları Kullanılarak Belirlenmesi. Fırat Üniversitesi Fen Bilimleri Dergisi. 2022;34(1):49-57.