Title: A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer 
Journal: Journal of Hydrology
Authors: Heesung Yoona, , Seong-Chun Junb, , Yunjung Hyuna,  Gwang-Ok Baea, and Kang-Kun Leea
Corresponding author: Kang-Kun Lee

Institute: 

a School of Earth and Environmental Sciences, Seoul National University, Seoul 151-747, Republic of Korea
b GeoGreen21 Co., Ltd., EnC Venture Dream Tower 2nd 901, Seoul 152-719, Republic of Korea
 
The original and creativity of paper: The paper compared the performance of artificial neural networks (ANN) and support vector machines (SVM) for ground water level prediction. 

Summary: 
Time-series forecasting models for the short-term ground water level fluctuation in a coastal aquifer using ANN and SVM have developed. Moreover, the performances of input structures and lead times have been compared and the results were show in Table 1.

Table 1 Performance of ANN and SVM

Contribution: This study introduced support vector machine model which can be an option for the researchers who work on prediction and classification problem.
  
By Monruedee Moonkhum
Email: moon@gist.ac.kr