Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation

  • Authors:

  • Ali A. Najah;A. El-Shafie;O. A. Karim;O. Jaafar

  • Affiliations:

  • University Kebangsaan Malaysia, UKM, Department of Civil & Structural Engineering, Bangi, Malaysia;University Kebangsaan Malaysia, UKM, Department of Civil & Structural Engineering, Bangi, Malaysia;University Kebangsaan Malaysia, UKM, Department of Civil & Structural Engineering, Bangi, Malaysia;University Kebangsaan Malaysia, UKM, Department of Civil & Structural Engineering, Bangi, Malaysia

  • Venue:
  • Neural Computing and Applications
  • Year:
  • 2012

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Abstract

This paper discusses the accuracy performance of training, validation and prediction of monthly water quality parameters utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was used to analyse the historical data that were generated through continuous monitoring stations of water quality parameters (i.e. the dependent variable) of Johor River in order to imitate their secondary attribute (i.e. the independent variable). Nevertheless, the data arising from the monitoring stations and experiment might be polluted by noise signals owing to systematic and random errors. This noisy data often made the predicted task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this study was to develop a technique that could enhance the accuracy of water quality prediction (WQP). Therefore, this study proposed an augmented wavelet de-noising technique with Neuro-Fuzzy Inference System (WDT-ANFIS) based on the data fusion module for WQP. The efficiency of the modules was examined to predict critical parameters that were affected by the urbanization surrounding the river. The parameters were investigated in terms of the following: the electrical conductivity (COND), the total dissolved solids (TDSs) and turbidity (TURB). The results showed that the optimum level of accuracy was achieved by making the length of cross-validation equal one-fifth of the data records. Moreover, the WDT-ANFIS module outperformed the ANFIS module with significant improvement in predicting accuracy. This result indicated that the proposed approach was basically an attractive alternative, offering a relatively fast algorithm with good theoretical properties to de-noise and predict the water quality parameters. This new technique would be valuable to assist decision-makers in reporting the status of water quality, as well as investigating spatial and temporal changes.