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A Supervised Machine Learning Approach for Analysis and Prediction of Water Quality

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Mobile Computing and Sustainable Informatics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 126))

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Abstract

The need to constantly monitor water quality is paramount as water pollution increases exponentially. In our study, data published by King County, Washington, USA, has been used to compare the performance of several algorithms which were used in predicting water quality index (WQI) and classifying water quality classification (WQC). Multilayer perceptron (MLP) performs most efficiently in both tasks with an RMSE of 0.76, R2 of 0.99, and classification accuracy of 97.1%. Feature analysis shows that phosphorus and ammonia nitrogen have considerable influence on predicting WQC and WQI, respectively, even though they were not directly used in the calculation of WQI. Sensitivity analysis done on the MLP model further shows that after removing the most important feature of turbidity as an input parameter, the model had an RMSE of 12.86 and an R2 of 0.84, respectively. It can be considered as one of the most significant parameters since it affects the WQI drastically.

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Author information

Authors and Affiliations

  1. Manipal Academy of Higher Education, Manipal, Karnataka, India

    Abhinav Mittra

  2. Sapthagiri College of Engineering Bangalore, Bengaluru, Karnataka, India

    Devanshu Singh

  3. Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India

    Anish Banda

Authors
  1. Abhinav Mittra
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  2. Devanshu Singh
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  3. Anish Banda
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Corresponding author

Correspondence to Abhinav Mittra .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. University of Applied Sciences, Athens, Greece

    Klimis Ntalianis

  3. Department of Computer Science, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

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Mittra, A., Singh, D., Banda, A. (2022). A Supervised Machine Learning Approach for Analysis and Prediction of Water Quality. In: Shakya, S., Ntalianis, K., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 126. Springer, Singapore. https://doi.org/10.1007/978-981-19-2069-1_18

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