A comparison of unsupervised and supervised machine learning algorithms to predict water pollutions

Nurnadiah Zamri, Mohammad Ammar Pairan, Wan Nur Amira Wan Azman, Siti Sabariah Abas, Lazim Abdullah, Syibrah Naim, Zamali Tarmudi, Miaomiao Gao

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
47 Downloads (Pure)


Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms' accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario.
Original languageEnglish
Pages (from-to)172-179
Number of pages8
JournalProcedia Computer Science
Early online date10 Sept 2022
Publication statusE-pub ahead of print - 10 Sept 2022


  • machine learning
  • deep learning
  • water pollutions
  • Terengannu river


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