Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

Wegayehu, Eyob Betru; Muluneh, Fiseha Behulu

doi:10.1155/2021/5172658

Cited by 21 publications

(12 citation statements)

References 50 publications

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“…The results reveal that the monthly hybrid model demonstrated good performance in absolute error and peak flow forecasting. Wegayehu and Muluneh [31] compared multi-layer perceptron (MLP), LSTM, and gated recurrent unit (GRU) with the proposed new hybrid models for short-term daily streamflow forecasting. The outcomes reveal that the integrated GRU layer substantially improved the simulation of streamflow time series.…”

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

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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.

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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

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“…For modeling, three different ANN methods were employed. In total, 80% of the data was used in training, and 20% of the data was used in testing [15,16], as shown in Figure 2.…”

Section: Study Area and Datamentioning

confidence: 99%

“…The dataset was divided into two portions for each model. The first 80% of the data was used for training and the remaining 20% for testing [15,16]. Before the training of the LSTM model, the data were standardized.…”

Section: Application and Comparison Of The Modelsmentioning

confidence: 99%

Forecasting of Streamflow and Comparison of Artificial Intelligence Methods: A Case Study for Meram Stream in Konya, Turkey

Asaad

Eryürük

2022

Sustainability

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The planning and management of water resources are affected by streamflow. The analysis of the sustainability of water resources has used well-grounded methods such as artificial neural networks, used for streamflow forecasting by researchers in recent years. The main aim of this study is to evaluate the performance of various methods for long-term forecasting from the data of the mean monthly streamflows between 1981 and 2017 from the Kucukmuhsine station on the Meram Stream in the Turkish province of Konya. For that reason, the multilayer perceptron (MLP), long short-term memory (LSTM), and adaptive neuro-fuzzy inference system (ANFIS) artificial intelligence techniques were employed in this study for the long-term forecasting of streamflow for 12 months, 24 months, and 36 months. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to evaluate the performance of the models developed to make predictions using the data from 1981 to 2017, and the Mann-Whitney test was applied to examine the differences between the actual data from 2018 to 2020 and each model’s forecasted results for those three years. The LSTM model showed superiority based on the values of R2 (calculated as 0.730) and RMSE (lowest value of 0.510), whereas the MLP yielded better prediction accuracy as reflected by the value of MAE (lowest value of 0.519). The ANFIS model did not have the best prediction ability for any of the criteria. In accordance with the Mann-Whitney test results, LSTM and MLP indicated no significant difference between the actual data from 2018 to 2020 and the forecasted values; whereas, there was a significant difference for the ANFIS model at a confidence level of 95%. The results showed that the LSTM model had a better prediction performance, surpassing the MLP and ANFIS models, when comparing mean monthly streamflow forecasts.

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“…Given the scarcity of water and the concerns about its future availability, it is essential to undertake studies that can aid in comprehending its dynamics for effective management. However, the variability of this water resource, attributed to climate change phenomena such as severe droughts, floods, storms, cyclones, and even human actions 1 exhibits chaotic, non-linear characteristics and high stochasticity 2 , making prediction complex and still a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Martinho,

Hippert,

Goliatt

2023

Sci Rep

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Accurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioinspired optimization algorithms (BOA) for short-term multistep streamflow forecasting. Specifically, we focus on applying XGB, MARS, ELM, EN, and SVR models and various BOA, including PSO, GA, and DE, for selecting model parameters. The performances of the resulting hybrid models are compared using performance statistics, graphical analysis, and hypothesis testing. The results show that the hybridization of BOA with ML models demonstrates significant potential as a data-driven approach for short-term multistep streamflow forecasting. The PSO algorithm proved superior to the DE and GA algorithms in determining the optimal hyperparameters of ML models for each step of the considered time horizon. When applied with all BOA, the XGB model outperformed the others (SVR, MARS, ELM, and EN), best predicting the different steps ahead. XGB integrated with PSO emerged as the superior model, according to the considered performance measures and the results of the statistical tests. The proposed XGB hybrid model is a superior alternative to the current daily flow forecast, crucial for water resources planning and management.

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Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

Cited by 21 publications

References 50 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

Forecasting of Streamflow and Comparison of Artificial Intelligence Methods: A Case Study for Meram Stream in Konya, Turkey

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

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