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Simulation of flood-prone areas using machine learning and GIS techniques in Samangan Province, Afghanistan

    Vahid Isazade Affiliation
    ; Abdul Baser Qasimi   Affiliation
    ; Abdulla Al Kafy Affiliation
    ; Pinliang Dong Affiliation
    ; Mustafa Mohammadi   Affiliation

Abstract

Flood events are the most sophisticated and damaging natural hazard compared to other natural catastrophes. Every year, this hazard causes human-financial losses and damage to croplands in different locations worldwide. 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. The Fishnet command was used to partition the information layers. Furthermore, each layer was converted into points, and this data was fed into the perceptron neural network along with the educational data obtained from Google Earth. In the perceptron neural network, the input layers have five neurons and 16 nodes, and the outputs showed that elevation had the lowest possible weight (R2 = 0.713) and flow direction had the highest weight (R2 = 0.913). This study demonstrated that combining GIS and artificial neural networks results in acceptable performance for simulating and modeling flood susceptible areas in different geographical locations and significantly helps prevent or reduce flood hazards.

Keyword : Flood, Perceptron artificial neural network, Digital elevation model, Samangan, Afghanistan

How to Cite
Isazade, V., Qasimi, A. B., Al Kafy, A., Dong, P., & Mohammadi, M. (2024). Simulation of flood-prone areas using machine learning and GIS techniques in Samangan Province, Afghanistan. Geodesy and Cartography, 50(1), 20–29. https://doi.org/10.3846/gac.2024.18555
Published in Issue
Apr 12, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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