Simulation of Flood-Prone Areas Using Machine Learning and GIS Techniques in Samangan Province, Afghanistan
Description:
This research employs a combination of artificial neural networks and geographic information systems (GIS) to simulate flood-vulnerable locations in the Samangan Province of Afghanistan. First, flood-influencing factors, such as soil, slope layer, elevation, flow direction, and land use/cover, were evaluated as influential factors in simulating flood-prone areas. These factors were imported into GIS software. This study demonstrated that combining GIS and artificial neural networks results in a…
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Date:
April 12, 2024
Creator:
Isazade, Vahid; Qasimi, Abdul Baser; Al Kafy, Abdulla; Dong, Pinliang & Mohammadi, Mustafa
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Partner:
UNT College of Science