Forecasting groundwater level using artificial neural networks

被引：2
|
作者：
Sreekanth, P. D. [1 ]
Geethanjali, N. [2 ]
Sreedevi, P. D. [3 ]
Ahmed, Shakeel [3 ]
Kumar, N. Ravi [4 ]
Jayanthi, P. D. Kamala [5 ]
机构：
[1] Natl Res Ctr Cashew, Puttur 574202, India
[2] Sri Krishnadevaraya Univ, Anantapur 515003, Andhra Pradesh, India
[3] Natl Geophys Res Inst, Hyderabad 500007, Andhra Pradesh, India
[4] Cent Plantat Crops Res Inst, Kasaragod 671124, India
[5] Indian Inst Hort Res, Bangalore 560089, Karnataka, India
来源：
CURRENT SCIENCE | 2009年 / 96卷 / 07期
关键词：
Artificial neural networks; back-propagation; feed-forward; forecasting; groundwater level; PREDICTION; MODELS;
D O I：
暂无
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
The performance of the artificial neural network (ANN) model, i.e. standard feed-forward neural network trained with Levenberg-Marquardt algorithm, was examined for forecasting groundwater level at Maheshwaram watershed, Hyderabad, India. The model efficiency and accuracy were measured based on the root mean square error (RMSE) and regression coefficient (R-2). The model provided the best fit and the predicted trend followed the observed data closely (RMSE = 4.50 and R-2 = 0.93). Thus, for precise and accurate groundwater level forecasting, ANN appears to be a promising tool.
引用
收藏
页码：933 / 939
页数：7
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