Fuzzy C-Mean Clustering Based Inference System for Saltwater Intrusion Processes Prediction in Coastal Aquifers

Dilip Kumar Roy () and Bithin Datta
Additional contact information
Dilip Kumar Roy: James Cook University
Bithin Datta: James Cook University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 1, No 24, 355-376

Abstract: Abstract Flow as well as salt transport processes of coastal aquifers are dependent on the density variation of water, and are complicated and extremely non-linear in nature. Simulation of these complicated systems to an acceptable degree of accuracy using a suitable surrogate model can be useful to achieve computational efficiency in modelling. In this study, a Sugeno type Fuzzy Inference System (FIS) is developed to predict salinity concentrations at specified monitoring locations as a result of groundwater pumping. Fuzzy c-mean clustering (FCM) algorithm is used to develop the FIS. Heterogeneity of aquifer is incorporated by using different hydraulic conductivities at different layers of the aquifer. A numerical simulation model, FEMWATER is adopted to generate the required patterns of inputs and outputs for initial training of the developed FIS. The FIS analyzed different combinations of antecedent transient pumping values, and returns concentration values at different monitoring locations. The performance of the FIS for training and validation datasets is evaluated by comparing the outputs obtained from the numerical simulation model. The trained and validated FIS is utilized as an approximate simulator of the coupled flow and salt transport processes. It is then used to forecast salt concentrations at different monitoring locations. Performance evaluation results indicate that the developed FIS can be applied to forecast salt concentrations at specified monitoring locations. The FIS is able to simulate the complex physical processes of heterogeneous coastal aquifers, and can be suitable for incorporation in a coupled simulation-optimization technique to develop optimum pumping strategy.

Keywords: Coastal aquifer; Salinity concentration; Surrogate model; Fuzzy logic; Fuzzy inference system; Fuzzy c-means clustering (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-016-1531-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1531-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-016-1531-3

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().