Identifying suitable dam sites using geospatial data and machine learning: a case study of the katsina-ala river in Benue State, Nigeria

Sustainable water resource management in Nigeria faces significant challenges due to suboptimal dam site selection, exacerbating flood risks and inefficient water utilization. This study addresses this gap by developing a robust geospatial and machine learning-integrated Multi-Criteria Decision Anal...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Earth science informatics Ročník 18; číslo 3; s. 497
Hlavní autoři:	Akajiaku, Ugochukwu Charles, Ohimain, Elijah Ige, Olodiama, Ebikombo-ere B., Eteh, Desmond Rowland, Winston, Ayebawanaemi G., Chukwuemeka, Prince, Otutu, Anslem O., Bamiekumo, Becky Peremoboere, Imoni, Okes
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025 Springer Nature B.V
Témata:	Accuracy Algorithms Analytic hierarchy process Community Construction sites Dam construction Dams Damsites Datasets Decision analysis Decision making Digital Elevation Models Earth and Environmental Science Earth Sciences Earth System Sciences Economic development Elevation Environmental impact Environmental risk Flood management Flood risk Floods Geographic information systems Hydroelectric power Hydrologic data Hydrology Information Systems Applications (incl.Internet) Land cover Land use Machine learning Multiple criterion Ontology Optimization Rainfall Rainfall data Remote sensing Rivers Simulation and Modeling Site selection Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Spatial analysis Spatial data Stream order Streams Subjectivity Support vector machines Sustainability management Sustainable development Topography Water consumption Water management Water resources management Water use Nigeria Flood risk mitigation GIS Deep learning Dam Machine learning Geospatial analysis Water access
ISSN:	1865-0473, 1865-0481
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!