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Neural network‐based screening for groundwater reclamation under uncertainty
Author(s) -
Ranjithan S.,
Eheart J. W.,
Garrett J. H.
Publication year - 1993
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/92wr02129
Subject(s) - hydraulic conductivity , realization (probability) , groundwater , aquifer , groundwater model , environmental remediation , groundwater remediation , artificial neural network , set (abstract data type) , computer science , groundwater flow , environmental science , data mining , soil science , statistics , engineering , artificial intelligence , mathematics , geotechnical engineering , ecology , contamination , biology , soil water , programming language
Uncertainty due to spatial variability of hydraulic conductivity is an important issue in the design of reliable groundwater remediation strategies. Using groundwater management models based on a stochastic approach to groundwater flow, where the log‐hydraulic conductivity is represented as a random field, is a frequently studied technique for the design of aquifer remediation in the presence of uncertainty. Such an approach employs the solution of a management model for a large set of equally probable realizations of the hydraulic conductivity. However, only a few out of the large set of realizations are critical to the final outcome of the design. The spatial distribution of the hydraulic conductivity values in a realization, and the degree of variation of the hydraulic conductivity values within a realization are identified as two important features that determine the level of criticalness of a realization. The association between the hydraulic conductivity pattern and the level of criticalness is not known explicitly and needs to be captured for efficient screening. The screening approach presented here utilizes the pattern classification capability of a neural network and its ability to learn from examples. It is shown that incorporation of only a few critical realizations in a groundwater management model can yield highly reliable remediation designs. The application of the screening tool in a pump‐and‐treat design problem is illustrated via two examples.