Modeling of Qaleh Rudkhan river sediment rate prediction, using LSTM neural network

Subject Areas : sediment

Mahbobeh Shadabi bejand ¹ , Ebrahim Amiri ²

1 - Phd student, Department of Water Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran. *(Corresponding Author)
2 - Prof, Department of Water Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.

Received: 2020-12-11 Accepted : 2021-08-24 Published : 2024-04-20

Keywords: Guilan province, instantaneous sediment, Qaleh Rudkhan River, recurrent neural networks, LSTM.,

Abstract :

Background and Objective: Proper estimation of the amount of sediment flowing in rivers is important as a data base for many river engineering designs and processes. Qaleh Rudkhan River is one of the most important water basins in the west of Gilan province. The most important branches of the basin are two branches named Gasht Rudkhan and Ghaleh Rudkhan. The river (Qaleh Rudkhan) is made up of two branches (Heydaralat) and (Nazaralat). Therefore, the purpose of this study was to model the prediction of sediment rate in Qaleh Rudkhan River using long short-term memory neural network (LSTM). Material and Methodology: In this research, the recorded Debi-sediment statistics related to the statistical period of 1381 to 1395 has been used. These statistics include daily instantaneous Debi in cubic meter per second and daily instantaneous sediment in ton per day, which are measured simultaneously. The data used to model the artificial neural network are Debi-sediment values the accuracy of the predictions was evaluated with three error criteria. Findings: The three criteria considered are AFE, FFE and n-AFE, respectively. Discussion and Conclusion: Among these criteria, the FFE criterion showed that the correlation between the model output and the measured sediment data is appropriate. As a result, the LSTM model has the appropriate accuracy to predict the amount of sediment in the two rivers of Qala-e-Rudkhan.

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