Determination of Water Pollution and Water Quality with Decision Trees

Authors: Çiğdem Bakır, Mecit Yüzkat

Abstract:

With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software used in the study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: Preprocessing of the data used, feature detection and classification. We tried to determine the success of our study with different accuracy metrics and the results were presented comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: Decision tree, water quality, water pollution, machine learning.

References:

[1] Budka, M., Gabrys, B., & Ravagnan, E. (2010). Robust predictive modelling of water pollution using biomarker data. Water research, 44(10), 3294-3308.
[2] Zharikova, E. P., Grigoriev, J. Y., & Grigorieva, A. L. (2022, February). Artificial Intelligence Methods for Detecting Water Pollution. In IOP Conference Series: Earth and Environmental Science (Vol. 988, No. 2, p. 022082). IOP Publishing.
[3] Priyadarshini, I., Alkhayyat, A., Obaid, A. J., & Sharma, R. (2022). Water pollution reduction for sustainable urban development using machine learning techniques. Cities, 130, 103970.
[4] Muhammad, S. Y., Makhtar, M., Rozaimee, A., Aziz, A. A., & Jamal, A. A. (2015). Classification model for water quality using machine learning techniques. International Journal of software engineering and its applications, 9(6), 45-52.
[5] Shafi, U., Mumtaz, R., Anwar, H., Qamar, A. M., & Khurshid, H. (2018, October). Surface water pollution detection using internet of things. In 2018 15th international conference on smart cities: improving quality of life using ICT & IoT (HONET-ICT) (pp. 92-96). IEEE.
[6] Chen, H., Chen, A., Xu, L., Xie, H., Qiao, H., Lin, Q., & Cai, K. (2020). A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agricultural Water Management, 240, 106303.
[7] Radhakrishnan, N., & Pillai, A. S. (2020, June). Comparison of water quality classification models using machine learning. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 1183-1188). IEEE. DOI: 10.1109/icces48766.2020.9137903
[8] Ragi, N. M., Holla, R., & Manju, G. (2019, May). Predicting water quality parameters using machine learning. In 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) (pp. 1109-1112). IEEE. DOI: 10.1109/rteict46194.2019.9016825
[9] Saghebian, S. M., Sattari, M. T., Mirabbasi, R., & Pal, M. (2014). Ground water quality classification by decision tree method in Ardebil region, Iran. Arabian journal of geosciences, 7, 4767-4777.
[10] Priyadarshini, I., Alkhayyat, A., Obaid, A. J., & Sharma, R. (2022). Water pollution reduction for sustainable urban development using machine learning techniques. Cities, 130, 103970.
[11] Wu, D., Jiang, J., Wang, F., Luo, Y., Lei, X., Lai, C., ... & Xu, M. (2023). Retrieving Eutrophic Water in Highly Urbanized Area Coupling UAV Multispectral Data and Machine Learning Algorithms. Water, 15(2), 354.
[12] Hu, Y., Du, W., Yang, C., Wang, Y., Huang, T., Xu, X., & Li, W. (2023). Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique. Frontiers of Environmental Science & Engineering, 17(5), 55.
[13] Tunca, S., Wilk, V., & Sezen, B. (2023). Defining Virtual Consumerism Through Content and Sentiment Analyses. Cyberpsychology, Behavior, and Social Networking.
[14] Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11(2), 612-619.
[15] Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., & Alazab, A. (2020). Hybrid intrusion detection system based on the stacking ensemble of c5 decision tree classifier and one class support vector machine. Electronics, 9(1), 173.
[16] Kherif, O., Benmahamed, Y., Teguar, M., Boubakeur, A., & Ghoneim, S. S. (2021). Accuracy improvement of power transformer faults diagnostic using KNN classifier with decision tree principle. IEEE Access, 9, 81693-81701.
[17] Yadav, D. C., & Pal, S. (2020). Prediction of thyroid disease using decision tree ensemble method. Human-Intelligent Systems Integration, 2, 89-95.