Physics-Informed Data-Driven Model for Predicting Streamflow: A Case Study of the Voshmgir Basin, Iranopen access
- Authors
- Parisouj, Peiman; Mokari, Esmaiil; Mohebzadeh, Hamid; Goharnejad, Hamid; Jun, Changhyun; Oh, Jeill; Bateni, Sayed M.
- Issue Date
- Aug-2022
- Publisher
- MDPI
- Keywords
- streamflow prediction; data-driven; hybrid modeling; LSTM model
- Citation
- APPLIED SCIENCES-BASEL, v.12, no.15
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 12
- Number
- 15
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67088
- DOI
- 10.3390/app12157464
- ISSN
- 2076-3417
2076-3417
- Abstract
- Accurate rainfall-runoff modeling is crucial for water resource management. However, the available models require more field-measured data to produce accurate results, which has been a long-term issue in hydrological modeling. Machine learning (ML) models have shown superiority in the hydrological field over statistical models. The primary aim of the present study was to advance a new coupled model combining model-driven models and ML models for accurate rainfall-runoff simulation in the Voshmgir basin in northern Iran. Rainfall-runoff data from 2002 to 2007 were collected from the tropical rainfall measuring mission (TRMM) satellite and the Iran water resources management company. The findings revealed that the model-driven model could not fully describe river runoff patterns during the investigated time period. The extreme learning machine and support vector regression models showed similar performances for 1-day-ahead rainfall-runoff forecasting, while the long short-term memory (LSTM) model outperformed these two models. Our results demonstrated that the coupled physically based model and LSTM model outperformed other models, particularly for 1-day-ahead forecasting. The present methodology could be potentially applied in the same hydrological properties catchment.
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