Document Type : Original Article
Authors
1
Department of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
2
Professor, Water and Environmental Engineering, Collage of Civil Engineering, , Shahrood University of Technology, Iran
3
Department of Remote sensing and GIS, Shahrood university of Technology. Shahrood. Iran
10.22125/iwe.2023.392196.1713
Abstract
Streamflow forecasting models play a crucial role in hydrological issues, such as the determination of reservoir inflows and flood forecasting. In this research, artificial intelligence hybrid models including ANFIS and GA-ANN have been used for short-term (daily) streamflow forecasting. This research aims to predict the outlet of the Latiyan basin, Tehran province, from 2017 to 2018. For this purpose, a snow-covered area (SCA) is obtained from the processing of Sentinel-2 optical satellite images. Then, in order to extract the effective snow, the fusion algorithm is applied for Sentinel-1 and 2 integrations. Finally, the artificial intelligence model with the help of the effective snow parameter along with other daily hydrometric and meteorological data including daily precipitation, temperature, and discharge is applied to forecast the daily outlet of the basin. Also, to improve the model performance, the seasonal index has been used to identify streamflow trends and better model training. The results showed that the prediction model using satellite data has improved its performance by 37%, which shows the direct effect of the snow parameter on the basin runoff. In addition, the trend of changes in the effective snow parameter has a favorable agreement with the flow trend of the basin, especially in the peak flows. Also, using seasonal information as an input parameter can improve the results of the prediction models by approximately 22%. In addition, the AI method based on fuzzy inference (ANFIS) showed better performance than the developed neural network method (GA-ANN) based on statistical indices.
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