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Назва предмета:

The use of the multi-criteria analysis for the exploration of surface irrigation potential zones : A case of the Didesa sub-basin, Abay basin, Ethiopia

Назва:
The use of the multi-criteria analysis for the exploration of surface irrigation potential zones : A case of the Didesa sub-basin, Abay basin, Ethiopia
Автори:
Tamiru, Habtamu
Dinka, Megersa O.
Теми:
analytic hierarchy process
AHP
key factor
multi-criteria analysis
MCA
physical land features
potential zones
Дата публікації:
2023
Видавець:
Instytut Technologiczno-Przyrodniczy
Мова:
angielski
закони:
CC BY-NC-ND: Creative Commons Uznanie autorstwa - Użycie niekomercyjne - Bez utworów zależnych 3.0 Unported
Джерело:
Journal of Water and Land Development; 2023, 58; 198--211
1429-7426
2083-4535
Постачальник контенту:
Biblioteka Nauki
статті
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This paper presents a study conducted using the Multi-Criteria Analysis (MCA) to explore surface irrigation potential zones in the Didesa sub-basin of the Abay basin in Ethiopia. Physical land features, such as land use / land cover (LULC), slope, soil depth, drainage, and road proximity, along with climate factors like rainfall and evapotranspiration, and population density, were identified as criteria for the exploration. The analytic hierarchy process (AHP) is a powerful structured decision-making technique commonly used for complex multi-criteria analysis problems where multiple criteria need to be considered. The importance of the criteria was prioritised and ranked in the analytic hierarchy process (AHP). Five qualitative-quantitative based surface irrigation potential zones were identified, namely highly suitable (48.40%), moderately suitable (27.26%), marginally suitable (13.27%), not suitable (4.91%), and irrigation constraints (6.16%). The consistency of the AHP technique in the exploration of surface irrigation potential zones is evaluated by the consistency index at CI = 0.011 and confirmed the correctness of weights assigned for the individual key factor in the AHP. The accuracy of the potential zones generated in the AHP was evaluated with ground-truth points and a supervised LULC classification map. Moreover, a good agreement was made among the classes with the kappa index (KI = 0.93). Therefore, the application of the MCA for the exploration of surface irrigation potential zones was successful, and the results of the study will be useful to strengthen the irrigation in the explored potential zones.

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