Spatial Prediction of the Groundwater Potential Using Remote Sensing Data and Bivariate Statistical-Based Artificial Intelligence Models

Yong Ye, Wei Chen (), Guirong Wang and Weifeng Xue
Additional contact information
Yong Ye: Xi’an University of Science and Technology
Wei Chen: Xi’an University of Science and Technology
Guirong Wang: Xi’an University of Science and Technology
Weifeng Xue: Shaanxi Coal and Chemical Technology Institute Co. Ltd.

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 14, No 7, 5494 pages

Abstract: Abstract Evaluation of the groundwater potential in a given region must be performed in groundwater resource management. The main purpose of this research was to simulate the groundwater potential in Wuqi County, China, based on five artificial intelligence models. The five data mining methods included the decision forest by penalizing attributes (forest PA) model, rudiment of the radial basis function network (RBFN) model, certainty factor (CF) model, logistic regression (LR) model and naïve Bayes (NB) model. According to local geographic environment characteristics, a total of sixteen conditioning factors were selected, the two-variable CF model was used to calculate the weight of factor subclasses, and multicollinearity analysis and the SVM method with different filter types were used to assess the independence and importance, respectively, of the conditioning factors. In addition, to obtain better model prediction results, the relevant modeling parameters of the forest PA and RBFN models were optimized via the tenfold cross-validation method in the modeling process. Finally, corresponding groundwater potential maps (GWPMs) of each model were generated, groundwater areas with a high potential were identified, and the consistency between the groundwater potential maps was compared by calculating the kappa statistical index. The area under the receiver operating characteristic curve (AUROC) was used to verify the accuracy and success rate of each model. The Wilcoxon signed-rank test method was also used to evaluate the significance of several benchmark numerical models. The results indicated that all five models achieved a satisfactory performance, and the models with optimized parameters (forest PA and RBFN models) realized greater predictive capabilities than those of the other unoptimized models. The results of this study could provide a certain guiding significance for rational management of groundwater systems and could contribute to the formulation of measures to ensure the best future use of groundwater energy.

Keywords: Groundwater; Remote sensing; Statistical analysis; Artificial intelligence model; Ensemble techniques (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-022-03307-w 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:36:y:2022:i:14:d:10.1007_s11269-022-03307-w

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

DOI: 10.1007/s11269-022-03307-w

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 ().