Development of PROMETHEE-Entropy data mining model for groundwater potentiality modeling: a case study of multifaceted geologic settings in south-western Nigeria

Mogaji, Kehinde Anthony; Atenidegbe, Olanrewaju Fred

doi:10.1007/s11600-023-01095-4

Artykuł - szczegóły

Tytuł artykułu

Development of PROMETHEE-Entropy data mining model for groundwater potentiality modeling: a case study of multifaceted geologic settings in south-western Nigeria

Autorzy

Mogaji Kehinde Anthony , Atenidegbe Olanrewaju Fred

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-023-01095-4

Warianty tytułu

Języki publikacji

Abstrakty

This work looks at developing an object-driven decision support system (DSS) model with the goal of improving the prediction accuracy of the present expert-driven DSS model in assessing groundwater potentiality. The database of remote sensing, geological, and geophysical information was constructed using the technological efficiency of GIS, data mining, and programming tools. Groundwater potential conditioning factors (GPCF) extracted from the datasets include lithology (Li), hydraulic conductivity (K), lineament density (Ld), transmissivity (T), and transverse resistance (TR) for groundwater potentiality mapping in a typical hard rock multifaceted geologic setting in south-western Nigeria. A Python-based entropy approach was used to objectively weight these factors. The weightage findings determined that the greatest and lowest given values for Ld and K were 0.6 and 0.03, respectively. The produced Python-based PROMETHEE-Entropy model algorithm was born through combining the weight findings with the Python-based PROMETHEE-II method. The groundwater potentiality model (GPM) map of the area was created using the model algorithm's outputs on the gridded raster of GPCF themes. Based on the suggested approach, the validated results of the created GPM maps using the Receiver Operating Characteristic (ROC) curve technique yielded an accuracy of 86%. An object-driven DSS model was created using the approaches that were used. The created object-driven model is a viable alternative to existing approaches in groundwater hydrology and aids in the automation of groundwater resource management in the research region.

Słowa kluczowe

groundwater potential geophysics remote sensing GIS PROMETHEE II entropy Python programming

potencjał wód podziemnych geofizyka teledetekcja GIS PROMETHEE II entropia język programowania Python

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 3

Strony

1957--1984

Opis fizyczny

Bibliogr. 87 poz.

Twórcy

autor

Mogaji Kehinde Anthony

Department of Applied Geophysics, Federal University of Technology, Akure, Nigeria

autor

Atenidegbe Olanrewaju Fred

ola00229@students.stir.ac.uk

Department of Applied Geophysics, Federal University of Technology, Akure, Nigeria
Present Address: Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, United Kingdom

https://orcid.org/0000-0002-5592-373X

Bibliografia

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