Volume 12, Issue 24 (9-2021)
J Watershed Manage Res 2021, 12(24): 97-108 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
isazade V, aliegigy Z. (2021). Simulation of Flood Prone Areas using Perceptron Neural Network and GIS (Study Area: Zolachai watershed, Salmas City). J Watershed Manage Res. 12(24), 97-108. doi:10.52547/jwmr.12.24.97
URL: http://jwmr.sanru.ac.ir/article-1-1125-en.html
URL: http://jwmr.sanru.ac.ir/article-1-1125-en.html
1- University of Tehran
Abstract: (3154 Views)
Extended Abstract
Introduction and Objective: Today, the flood phenomenon is one of the most complex and dangerous events that, more than other natural disasters, leads to human and financial losses and destruction of agricultural lands in different parts of the world every year.
Material and Methods: Due to the flooding of Zolachai Watershed, Salmas County, it seems necessary to study and simulate the risk of floods in this area. Therefore, in this study, a combination of artificial perceptron neural network (MLP) and GIS has been used. First, the effective parameters in simulating flood areas such as: slope layer, height, flow direction, soil and land use were examined and these information layers were entered into GIS software. The information layers were processed with the Fishnet command. And each layer became a point.This data, along with the educational data received from Google Earth, was introduced to the perceptron neural network.
Results: In the perceptron neural network, the input layers including 5 neurons and 16 nodes entered the model and the results showed that the height has the lowest weight (R2=0.713) and the highest weight related to the flow direction (R2=0.913) in simulating the Zolachai Watershedflood., Is the city of Salmas.
Conclusion: It can be said that the combination of GIS and artificial neural network can be very useful for modeling and simulating floods in different spatial environments to prevent and reduce environmental hazards.
Introduction and Objective: Today, the flood phenomenon is one of the most complex and dangerous events that, more than other natural disasters, leads to human and financial losses and destruction of agricultural lands in different parts of the world every year.
Material and Methods: Due to the flooding of Zolachai Watershed, Salmas County, it seems necessary to study and simulate the risk of floods in this area. Therefore, in this study, a combination of artificial perceptron neural network (MLP) and GIS has been used. First, the effective parameters in simulating flood areas such as: slope layer, height, flow direction, soil and land use were examined and these information layers were entered into GIS software. The information layers were processed with the Fishnet command. And each layer became a point.This data, along with the educational data received from Google Earth, was introduced to the perceptron neural network.
Results: In the perceptron neural network, the input layers including 5 neurons and 16 nodes entered the model and the results showed that the height has the lowest weight (R2=0.713) and the highest weight related to the flow direction (R2=0.913) in simulating the Zolachai Watershedflood., Is the city of Salmas.
Conclusion: It can be said that the combination of GIS and artificial neural network can be very useful for modeling and simulating floods in different spatial environments to prevent and reduce environmental hazards.
Type of Study: Research |
Subject:
سنجش از دور و سامانه های اطلاعات جغرافيايی
Received: 2020/12/24 | Accepted: 2021/04/21
Received: 2020/12/24 | Accepted: 2021/04/21
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
