Improving water quality index prediction in Perak River basin Malaysia through a combination of multiple neural networks

Z. Ahmad, N. A. Rahim, Alireza Bahadori, Jie Zhang
  • International Journal of River Basin Management, November 2016, Taylor & Francis
  • DOI: 10.1080/15715124.2016.1256297

Forecasting of Water Quality Index (WQI) using machine learning

What is it about?

Application of multiple neural networks (MNN) in predicting WQI in Malaysia

Why is it important?

Highly dynamics input for WQI and MNN is able to dealt with it and able to predict the WQI accurately. It can become a platform for monitoring using cloud etc .


Dr Zainal Ahmad
Universiti Sains Malaysia

Dealing with real data is not that easy as compared to secondary data which is less noise etc as compare to real one. However MNN is able to dealt with noise in the measurement as well as dealt with highly correlated input data to predict the WQI accurately. This approach can be extended to other monitoring as well like API water discharge etc.

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The following have contributed to this page: Dr Zainal Ahmad