Comparison of Groundwater Level Estimation Using Neuro-fuzzy and Ordinary Kriging

被引：70
|
作者：
Kholghi, M. [1 ]
Hosseini, S. M. [1 ]
机构：
[1] Univ Tehran, Dept Irrigat & Reclamat Engn, Karaj, Iran
来源：
ENVIRONMENTAL MODELING & ASSESSMENT | 2009年 / 14卷 / 06期
关键词：
Geostatistics; Kriging; Groundwater level; ANFIS; ADAPTIVE-NETWORK; SYSTEMS; CONDUCTIVITY; INFORMATION; MODEL;
D O I：
10.1007/s10666-008-9174-2
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Water level in aquifer plays the main role in groundwater modeling as one of the input data. In practice, due to aspects of time and cost, data monitoring of water levels is conducted at a limited number of sites, and interpolation technique such as kriging is widely used for estimation of this variable in unsampled sites. In this study, the efficiency of the ordinary kriging (OK) and adaptive network-based fuzzy inference system (ANFIS) was investigated in interpolation of groundwater level in an unconfined aquifer in the north of Iran. The results showed that ANFIS model is more efficient in estimating the groundwater level than OK.
引用
收藏
页码：729 / 737
页数：9
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