Open Access
Open access
volume 2023 pages 1-18

AHP and Machine Learning-Based Military Strategic Site Selection: A Case Study of Adea District East Shewa Zone, Ethiopia

Amanuel Kumsa Bojer 1
Firesew Feyisso Woldesilassie 1
Taye Girma Debelee 1, 2
Samuel Rahimeto Kebede 1
Sintayehu Zekarias Esubalew 1
Publication typeJournal Article
Publication date2023-07-10
scimago Q3
wos Q4
SJR0.390
CiteScore5.4
Impact factor1.1
ISSN1687725X, 16877268
Electrical and Electronic Engineering
Instrumentation
Control and Systems Engineering
Abstract

In dynamic situations, multicriteria decision-making (MCDM) for classification frequently takes place. The selection of suitable land sites and clear criteria is one of the most significant challenges in strategic military site selection around the world and at the national level. The main objectives of the present study are to find out the strategic military site land suitability in Adea District by using AHP, GIS, and machine learning algorithms. To determine the subject weights of criteria, a novel approach for prioritizing the intuitionistic fuzzy judgement matrix is proposed. An optimization model is developed to determine the objective weights that maximize the distance of each alternative to the negative-ideal solution. Machine learning methods are mainly employed for filtering, interpreting, and predicting information from satellite image data. The random forest analysis method is used for patterning the military strategic site selection in the Adea District. The results of the study were able to create models and submodels of military strategic site land suitability using the weighted linear combination (WLC) mechanism. From the entire research area woreda, 87.02 km2 (9.3%), 439.11 km2 (46.9%), 114.59 km2 (12.2%), 196.48 km2 (20.9%), and 99.19 km2 (10.6%) have not suitable, marginally suitable, moderately suitable, suitable, and highly suitable conditions, respectively, for a strategic military site. Changes in the six classes of land use/land cover were assessed with an overall accuracy of above 97% and an overall kappa statistic of 96%. Finally, the study actually suggests that ML, RS, GIS, and AHP algorithms are beneficial for decision-makers (DMs) that will enhance the procedure for strategic military site, supported by multicriteria decision-making and its applications for the study area’s land suitability. Furthermore, security and military authorities will be able to propose a planning protocol and suitable sites for strategic military site selection in the near future based on the study’s basic methodological application and main findings.

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GOST Copy
Bojer A. K. et al. AHP and Machine Learning-Based Military Strategic Site Selection: A Case Study of Adea District East Shewa Zone, Ethiopia // Journal of Sensors. 2023. Vol. 2023. pp. 1-18.
GOST all authors (up to 50) Copy
Bojer A. K., Woldesilassie F. F., Debelee T. G., Kebede S. R., Esubalew S. Z. AHP and Machine Learning-Based Military Strategic Site Selection: A Case Study of Adea District East Shewa Zone, Ethiopia // Journal of Sensors. 2023. Vol. 2023. pp. 1-18.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1155/2023/6651486
UR - https://doi.org/10.1155/2023/6651486
TI - AHP and Machine Learning-Based Military Strategic Site Selection: A Case Study of Adea District East Shewa Zone, Ethiopia
T2 - Journal of Sensors
AU - Bojer, Amanuel Kumsa
AU - Woldesilassie, Firesew Feyisso
AU - Debelee, Taye Girma
AU - Kebede, Samuel Rahimeto
AU - Esubalew, Sintayehu Zekarias
PY - 2023
DA - 2023/07/10
PB - Hindawi Limited
SP - 1-18
VL - 2023
SN - 1687-725X
SN - 1687-7268
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2023_Bojer,
author = {Amanuel Kumsa Bojer and Firesew Feyisso Woldesilassie and Taye Girma Debelee and Samuel Rahimeto Kebede and Sintayehu Zekarias Esubalew},
title = {AHP and Machine Learning-Based Military Strategic Site Selection: A Case Study of Adea District East Shewa Zone, Ethiopia},
journal = {Journal of Sensors},
year = {2023},
volume = {2023},
publisher = {Hindawi Limited},
month = {jul},
url = {https://doi.org/10.1155/2023/6651486},
pages = {1--18},
doi = {10.1155/2023/6651486}
}