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:: Volume 8, Issue 3 (1-2021) ::
jgit 2021, 8(3): 61-82 Back to browse issues page
Modeling the potential of Sand and Dust Storm sources formation using time series of remote sensing data, fuzzy logic and artificial neural network (A Case study of Euphrates basin)
Ramin Papi, Meysam Argany *, Shahab Moradipour, Masoud Soleimani
University of Tehran
Abstract:   (587 Views)
Sand and Dust Storms (SDS) are known as one of the most common environmental problems in arid and semi-arid regions of the world. This phenomenon is harmful to human health as well as to economy. Over the past two decades, SDS have been increasing on a local, regional and even global scale. The Euphrates Basin is recognized as one of the most active SDS sources in the world. The first step in managing this environmental phenomenon, is to identify dust storm sources. The aim of this study is mapping the potential sources of SDS in the Euphrates basin by using Multi-Layer Perceptron Neural Network. In the first step, the long-term time series of which is data, related to key environmental parameters affecting the occurrence of SDS including: soil moisture, soil temperature, soil texture, land surface temperature, wind speed, precipitation, evapotranspiration, dusty months, land use population, pressure, the identified elevation and slope were used as artificial neural network model inputs. Using the visual interpretation of 2500 MODIS images in natural color composite, 190 SDS centers were identified visually and introduced to the neural network as training points. 70% of the points (133 points) and 30% of them (57 points) were used for training, testing and validation of model, respectively. After running the model, the estimated mean squared error (MSE) was equal to 0.1, which indicats acceptable accuracy of the neural network model in mapping the potential SDS sources. The results show that, 147000 km2 of the basin is prone to the formation of SDS sources, which mainly include low rainfall, dry and barren areas of the basin.
Keywords: Sand and Dust Storm (SDS), Remote Sensing, Time Series, Artificial Neural Network, Euphrates Basin
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Type of Study: Research | Subject: RS
Received: 2020/01/4 | Accepted: 2020/12/16 | Published: 2021/01/19
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Papi R, Argany M, Moradipour S, Soleimani M. Modeling the potential of Sand and Dust Storm sources formation using time series of remote sensing data, fuzzy logic and artificial neural network (A Case study of Euphrates basin). jgit. 2021; 8 (3) :61-82
URL: http://jgit.kntu.ac.ir/article-1-737-en.html


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Volume 8, Issue 3 (1-2021) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
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