direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

There is no English translation for this web page.

Journal articles

Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models
Citation key https://doi.org/10.1002/tqem.21775
Author Zounemat-Kermani, Mohammad and Mahdavi-Meymand, Amin and Fadaee, Marzieh and Batelaan, Okke and Hinkelmann, Reinhard
Year 2021
DOI https://doi.org/10.1002/tqem.21775
Journal Environmental Quality Management
Volume n/a
Number n/a
Abstract Abstract Reliable and accurate modeling of groundwater quality is an important element of sustainable groundwater management of productive aquifers. In this research, specific conductance (SC) of groundwater is predicted based on different individual and integrative machine learning, adaptive neuro-fuzzy inference system (ANFIS), and nonlinear mathematical models. For developing the integrative models, the well-known particle swarm optimization (PSO) and novel manta ray foraging optimization (MRFO) heuristic algorithms are embedded in the models. Presenting different univariate, bivariate, and multivariate input scenarios, the parameters used to develop and validate the models include groundwater level, salinity, and water temperature at an observation well near Florida City. The findings reveal that applying more independent parameters (multivariate scenario) enhances the performance of both the mathematical and machine learning models. Even though the mathematical models present an acceptable performance for the prediction of SC (index of agreement, IA, equals 0.933), the ANFIS models provide the most accurate SC predictions (IA = 0.943). Both the PSO and MRFO algorithms improved the prediction capability of the ANFIS models with, respectively, 13\% and 5\% for the RMSE.
Bibtex Type of Publication Journal
Link to publication Download Bibtex entry

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions