Enhancing Water Sustainability with AI methods: Analysis and Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach

Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach

Authors

  • Biplov Paneru Dept.of Electronics & communication, Nepal Engineering College
  • Bishwash Paneru Dept. of Electronics and Communication Engineering, Pokhara University
  • Sanjog Chhetri Sapkota Nepal Research and collaboration centre

DOI:

https://doi.org/10.34010/injiiscom.v6i2.13396

Keywords:

Machine Learning, regression, classification algorithm, catboost, MLP, MLP-GRU, LSTM-GRU

Abstract

Water quality is a crucial concern worldwide, including in Nepal, where efficient monitoring is essential for safe drinking water and preventing waterborne illnesses. This study employs machine learning to analyze and forecast the seasonal water quality index (WQI) of Nepalese well water. Hybrid models with nested cross-validation were introduced, using methods like CatBoost, Decision Tree, Logistic Regression, MLP-GRU, and LSTM-GRU hybrids. Performance metrics included R², accuracy, and RMSE. CatBoost achieved the highest classification accuracy (99.35%), while the LSTM-GRU hybrid excelled in capturing complex temporal patterns. Nested cross-validation demonstrated 96.13% accuracy with low standard deviation. Additionally, SHAP analysis identified key predictive factors using the SVM model. This research highlights machine learning’s potential in predicting and managing water quality effectively

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Published

2024-12-24

Issue

Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025 (ONLINE FIRST)

Section

Articles

How to Cite

[1]
“Enhancing Water Sustainability with AI methods: Analysis and Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach: Prediction of Seasonal Water Quality of Nepal Using Machine Learning Approach”, Int. J. Inform. Inf. Sys. and Comp. Eng., vol. 6, no. 2, pp. 166–185, Dec. 2024, doi: 10.34010/injiiscom.v6i2.13396.