A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Mojtaba Poursaeid (), Amir Houssain Poursaeid and Saeid Shabanlou
Additional contact information
Mojtaba Poursaeid: MPO-Plan and Budget Organization
Amir Houssain Poursaeid: Lorestan University
Saeid Shabanlou: Islamic Azad University, Kermanshah Branch

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 5, No 1, 1499-1519

Abstract: Abstract Today, various methods have been developed to extract drinking water resources, which scientists use to simulate the quantitative and qualitative water resources parameters. Due to Iran's geographical and climatic characteristics, this region is located on the drought belt in Asia. In this research, some Artificial Intelligence (AI) and mathematical models have been used for groundwater level prediction. The AI models used for this research are Extreme Learning Machine (ELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) model. In this study, simultaneously, these models were used to simulate and estimate groundwater level (GWL). The database used in the simulation is the data related to the Total Dissolved Solids (TDS), Electrical Conductivity (EC), Salinity (S), and Time (t) parameters. The results showed that ELM was more accurate than other methods. In Uncertainty Wilson Score Method (UWSM) analysis, ELM had an Underestimation performance and was determined as the more precise model.

Keywords: Extreme Learning Machine; Least square Support Vector Machine; Multiple Linear Regression; Adaptive Neuro-Fuzzy Inference System; MLR; LSSVR; ELM; ANFIS; Water Parameters; Quantity Parameters (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-022-03070-y 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:5:d:10.1007_s11269-022-03070-y

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

DOI: 10.1007/s11269-022-03070-y

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 ().