| Predicting Groundwater Level Using the Soft Computing Tool: An Approach for Precision Enhancement |
| Pagadala Damodaram S 1†, Pagadala Damodaram S 2, and Shakeel Ahmed 1 |
1National Geophysical Research Institute, Council of Scientific and Industrial Research, Hyderabad 500007, Andhra Pradesh, India
2National Academy of Agricultural Research Management, Indian Council of Agricultural Research, Rajendranagar, Hyderabad 500407, Andhra Pradesh, India |
Corresponding Author:
Pagadala Damodaram S ,Tel: +91-4023434700(2641), Fax: +91-4027171564, Email: pd_sreedevi@yahoo.co.in
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Received: September 3, 2012; Accepted: October 10, 2012. |
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| ABSTRACT |
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Monitoring a non-linear phenomenon—such as the groundwater levels in an aquifer—by cost-effective techniques is quite a difficult task. To overcome these limitations, soft computing tools are increasingly being used to predict groundwater levels with high accuracy. In the present study, a soft computing tool called support vector machine (SVM) was employed for predicting the groundwater levels jointly using weather parameters, at Maheshwaram watershed, Hyderabad, Andhra Pradesh, India. The accuracy of this approach was established based on statistical tools termed the regression coefficient, root mean square error, Nash-Sutcliffe coefficient, and error variation. For performance evaluation, the model outputs were compared with traditional statistical multiple regression (SMR) model outputs, and it was found that the SVR method offers better prediction than does SMR. |
| Keywords:
Groundwater levels prediction | Statistical multiple regression analysis | Support vector machine |
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