Abstract
Streamflow forecasting is a critical aspect of water resource management, particularly in regions where surface water is scarce. In this study, we present an analysis of streamflow forecasting models and their associated uncertainty assessment within a major basin in Iran. Our approach involved the utilization of five data-driven models, including Adaptive-Network-based Fuzzy Inference System (ANFIS), alongside two conceptual models: the Soil and Water Assessment Tool (SWAT) and the Identification of Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow data (IHACRES). To assess model accuracy, we employed error metrics and quantified uncertainty through the 95th Percentile Prediction Uncertainty (95PPU) range, p factor, and d factor. Our results demonstrated that during the validation phase, all seven models display satisfactory performance, with root mean squared errors (RMSE) consistently below 2.58 m3/s. A notable observation was the performance of IHACRES, which, despite utilizing only six parameters, performed comparably to the SWAT model, yielding a validation RMSE of 1.81 compared to 1.80 m3/s. Turning to model uncertainty, our findings revealed that during data-driven model training, a minimum of 54% of the observed data fell within the 95PPU range, even with d factors falling below one. In the testing phase, at least 50% of the observed data resided within the 95PPU range, with a maximum d factor of 0.48. For the SWAT model, calibration and validation p factors were determined to be 0.42 and 0.25, respectively, indicating a comparatively higher level of uncertainty when compared to data-driven models. Collectively, our results underscored ANFIS as the standout model in terms of reliability, boasting a p factor of 79%.
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The data used in this study are openly available for download from the following URL: https://data.wrm.ir/.
Abbreviations
- 95PPU:
-
95Th Percentile Prediction Uncertainty
- ANFIS:
-
Adaptive-Network-based Fuzzy Inference System
- DEMs:
-
Digital elevation models
- GFF:
-
Generalized feed forward
- IHACRES:
-
Identification of Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow data
- MLP:
-
Multilayer perceptron
- NS:
-
Nash–Sutcliffe model efficiency coefficient
- PBIAS:
-
Percent bias
- RBF:
-
Radial basis function
- RMSE:
-
Root mean square error
- RSR:
-
RMSE-observations standard deviation ratio
- SUFI-2:
-
Sequential Uncertainty Fitting program
- SVM:
-
Support vector machine
- SWAT:
-
Soil and Water Assessment Tool
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Ashrafzadeh, A., Salehpoor, J. & Lotfirad, M. Comparative analysis of data-driven and conceptual streamflow forecasting models with uncertainty assessment in a major basin in Iran. Int J Energ Water Res (2024). https://doi.org/10.1007/s42108-023-00276-7
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DOI: https://doi.org/10.1007/s42108-023-00276-7