A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates

Kilinç, Hüseyin Çağan; Haznedar, Bülent

doi:10.3390/w14010080

Cited by 46 publications

(23 citation statements)

References 35 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, it is anticipated that the pressure on water resources will intensify in the following years due to the indirect impacts of climate change, such as the melting of glaciers, rises in sea level, irregular precipitation, etc. [3]. In this context, water management, the conservation of water resources, and managing water consumption are among the most crucial issues in relation to water resources.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

Self Cite

View full text Add to dashboard Cite

The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

show abstract

Section: Introductionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, one of the reasons for choosing PSO as the optimization algorithm to search for the appropriate values of the LSTM parameters is that, when compared to genetic algorithms, it performs with real numbers and has some benefits such as not needing binary coding to make calculations. Statistical evaluation criteria, which are among the basic statistical evaluation methods, were used to measure the model's performance [56]. The results obtained show that in the proposed PSO-LSTM approach, the estimation errors of the flow data are quite low compared to the other models used in the study.…”

Section: Discussionmentioning

confidence: 99%

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Kilinç¹

2022

Water

Self Cite

View full text Add to dashboard Cite

Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models.

show abstract

“…LSTM RNN is suitable for various water related variables with time series e.g., river flow, groundwater table, precipitation, etc. [32,64,[69][70][71][72].…”

Section: Long Short-term Memory (Lstm) Recurrent Neuralmentioning

confidence: 99%

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Mehedi¹,

Khosravi²,

Yazdan³

et al. 2022

Preprint

View full text Add to dashboard Cite

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

show abstract

A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates

Cited by 46 publications

References 35 publications

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Contact Info

Product

Resources

About