Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

22-05-2020 | Issue 8/2020

Water Resources Management 8/2020

Strategies for Learning Groundwater Potential Modelling Indices under Sparse Data with Supervised and Unsupervised Techniques

Journal:
Water Resources Management > Issue 8/2020
Authors:
V. Karimi, R. Khatibi, M. A. Ghorbani, D. Tien Bui, S. Darbandi
Important notes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Mapping for Groundwater Potential Indices (GPI) is investigated for study areas with sparse data by the customary ten general-purpose data layers with a scoring system of rates and weights but assigning their values give rise to subjectivity. Learning rates/weights from site-specific data reduces subjectivity through unsupervised models. The use of supervised models requires target values, and the paper derives their values from the record at all the productive wells by developing a binary classification model. The paper formulates an Inclusive Multiple Modelling (IMM) strategy to learn from the site data at two levels: at Level 1: two unsupervised ‘base’ models and four supervised ‘base’ models are investigated; at Level 2 the IMM strategies include a supervised ‘combiner’ model, which uses outputs of unsupervised base models; as well as an unsupervised ‘combiner’ model, which uses outputs of supervised base models. Performance metrics are derived by the Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC). The results show that unsupervised learning at Level 2 (using supervised base models) may reduce subjectivity but even supervised learning at Level 1 can be effective in extracting essential information from target values. Although unsupervised models would extract marginal information from models at Level 1, a supervised model at Level 2 can extract good information from unsupervised models at Level 1.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 8/2020

Water Resources Management 8/2020 Go to the issue