Abstract
This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.
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Translated from Meteorologiya i Gidrologiya, 2021, No. 6, pp. 66-76. https://doi.org/10.52002/0130-2906-2021-6-66-76.
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Esmaeelzadeh, R., Golian, S., Sharific, S. et al. Enhanced Long-term and Snow-based Streamflow Forecasting by Artificial Intelligent Methods Using Satellite Imagery and Seasonal Information. Russ. Meteorol. Hydrol. 46, 396–402 (2021). https://doi.org/10.3103/S1068373921060066
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DOI: https://doi.org/10.3103/S1068373921060066