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Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record

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  • Mohamad Sakizadeh

    (Shahid Rajaee Teacher Training University, Tehran, Iran)

  • Hassan Rahmatinia

    (Shahid Rajaee Teacher Training University, Tehran, Iran)

Abstract

The objective of this study was to consider the efficiency of support vector machine (SVM) and artificial neural network (ANN) for the classification and prediction of groundwater quality using a small data record in Malayer, Iran. For this purpose, 14 groundwater quality variables that had been collected from 27 groundwater sampling wells were used. Cluster analysis discriminated the total sampling stations into two groups. The classification was implemented by SVM using polynomial and RBF kernel methods. The respective sensitivity and specificity of this model were 0.89 and 0.80 while that of positive predictive value and negative predictive value were 0.89 and 0.86, respectively. The prediction of water quality index (WQI) was implemented using ANN. Despite the high correlation coefficient between the predicted and observed values of WQI(r = 0.90), the generalization ability of this model was low(r = 0.60) indicating the over-fitting of the model to the training data set.

Suggested Citation

  • Mohamad Sakizadeh & Hassan Rahmatinia, 2017. "Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 8(4), pages 37-53, October.
    • Handle: RePEc:igg:jaeis0:v:8:y:2017:i:4:p:37-53
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    Cited by:

    1. Naser Shiri & Jalal Shiri & Zaher Mundher Yaseen & Sungwon Kim & Il-Moon Chung & Vahid Nourani & Mohammad Zounemat-Kermani, 2021. "Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.

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