Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations

被引：109

作者：

Shiri, Jalal ^{[1
]}

Kisi, Ozgur ^{[2
]}

机构：

[1] Univ Tabriz, Fac Agr, Water Engn Dept, Tabriz, Iran

[2] Erciyes Univ, Hydraul Div, Dept Civil Engn, Kayseri, Turkey

来源：

COMPUTERS & GEOSCIENCES | 2011年 / 37卷 / 10期

关键词：

Groundwater depth fluctuation; Genetic programming; Neuro-fuzzy; Forecast; COMPUTING TECHNIQUE; MODEL; NETWORKS; ALGORITHMS;

D O I：

10.1016/j.cageo.2010.11.010

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper investigates the ability of genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) techniques for groundwater depth forecasting. Five different GP and ANFIS models comprising various combinations of water table depth values from two stations, Bondville and Perry, are developed to forecast one-, two- and three-day ahead water table depths. The root mean square errors (RMSE), scatter index (SI), Variance account for (VAF) and coefficient of determination (R-2) statistics are used for evaluating the accuracy of models. Based on the comparisons, it was found that the GP and ANFIS models could be employed successfully in forecasting water table depth fluctuations. However, GP is superior to ANFIS in giving explicit expressions for the problem. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：1692 / 1701

页数：10

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