A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM, and CNN-GRU-LSTM

Vatanchi, Sajjad M.; Etemadfard, Hossein; Maghrebi, Mahmoud Faghfour; Shad, Rouzbeh

doi:10.21203/rs.3.rs-1443377/v1

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…An analysis of the univariate streamflow predictions by Zhang et al [12] found that data-driven models are widely used because of their simplicity and minimal data needs. The most frequently artificial intelligence (AI) models used in streamflow forecasting are neural-based fuzzy inference systems (ANFIS) [13], support vector machines (SVM) [14], and artificial neural networks (ANN) [15]. As a simple operational model, ANN offers significant non-linear mapping capabilities [10].…”

Section: Introductionmentioning

confidence: 99%

Applicability of ANN Model and CPSOCGSA Algorithm for Multi-Time Step Ahead River Streamflow Forecasting

et al. 2022

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Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications.

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Section: Introductionmentioning

confidence: 99%

Applicability of ANN Model and CPSOCGSA Algorithm for Multi-Time Step Ahead River Streamflow Forecasting

et al. 2022

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Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall–snowmelt regime Munzur River, Türkiye

Katipoğlu

2023

Environ Sci Pollut Res

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Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain

Farfán-Durán,

Cea

2024

Earth Sci Inform

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Accurate hourly streamflow prediction is crucial for managing water resources, particularly in smaller basins with short response times. This study evaluates six deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrids (CNN-LSTM, CNN-GRU, CNN-Recurrent Neural Network (RNN)), across two basins in Northwest Spain over a ten-year period. Findings reveal that GRU models excel, achieving Nash-Sutcliffe Efficiency (NSE) scores of approximately 0.96 and 0.98 for the Groba and Anllóns catchments, respectively, at 1-hour lead times. Hybrid models did not enhance performance, which declines at longer lead times due to basin-specific characteristics such as area and slope, particularly in smaller basins where NSE dropped from 0.969 to 0.24. The inclusion of future rainfall data in the input sequences has improved the results, especially for longer lead times from 0.24 to 0.70 in the Groba basin and from 0.81 to 0.92 in the Anllóns basin for a 12-hour lead time. This research provides a foundation for future exploration of DL in streamflow forecasting, in which other data sources and model structures can be utilized.

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A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM, and CNN-GRU-LSTM

Cited by 4 publications

References 30 publications

Applicability of ANN Model and CPSOCGSA Algorithm for Multi-Time Step Ahead River Streamflow Forecasting

Applicability of ANN Model and CPSOCGSA Algorithm for Multi-Time Step Ahead River Streamflow Forecasting

Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall–snowmelt regime Munzur River, Türkiye

Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain

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