scholarly journals Parameter Identification Procedures of Pumping Test Data in Confined Aquifers

1989 ◽  
Vol 29 (2) ◽  
pp. 159-168 ◽  
Author(s):  
Iichiro Kono ◽  
Makoto Nishigaki ◽  
Yuji Takeshita
10.29007/11v5 ◽  
2018 ◽  
Author(s):  
M. Tamer Ayvaz ◽  
Gurhan Gurarslan

The main objective of this study is to propose a linked simulation-optimization approach to determine the parameters of the confined and leaky-confined aquifers from the results of the pumping tests. In the simulation part of the proposed approach, the drawdowns at the given monitoring points and times are calculated by considering Theis and Hantush approaches for confined and leaky-confined aquifers, respectively. This simulation part is then integrated with a hybrid optimization approach where global exploration feature of the harmony search (HS) and strong local search capability of the generalized reduced gradient (GRG) approach of the spreadsheet Solver add-in are mutually integrated. The performance of the proposed approach is evaluated by considering two pumping test data for the confined and leaky-confined aquifers. Identified results indicated that the hybrid HS-Solver optimization approach provides better results than those obtained by using both curve matching and stand-alone HS approaches.


Hydrology ◽  
2017 ◽  
pp. 333-353
Author(s):  
Ian Watson ◽  
Alister D. Burnett

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.


1985 ◽  
Vol 25 (3) ◽  
pp. 127-132 ◽  
Author(s):  
Takeshi Sato ◽  
Kano Ueshita
Keyword(s):  

2017 ◽  
Vol 226 ◽  
pp. 44-51 ◽  
Author(s):  
Yong-Xia Wu ◽  
Jack Shuilong Shen ◽  
Wen-Chieh Cheng ◽  
Takenori Hino

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