Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin

The conditions which affect the sustainability of water cause a number of serious environmental and hydrological problems. Effective and correct management of water resources constitutes an effective and important issue among scales. In this sense, a precise estimation of streamflow time series in rivers is one of the most important issues in optimal management of surface water resources. Therefore, a hybrid method combining particle swarm algorithm (PSO) and long short-term memory networks (LSTM) are proposed to predict flow with data obtained from different flow measurement stations. In this respect, the data gathered from three Flow Measurement Stations (FMS) from Zamanti and Eglence rivers located on Seyhan Basin are utilized. Besides, the proposed LSTM-PSO method is compared to an adaptive neuro-fuzzy inference system (ANFIS) and the LSTM benchmark model to demonstrate the performance achievement of proposed method. The prediction performances of the developed hybrid model and the others are tested on the determined stations. The forecasting performances of the models are determined with RMSE, MAE, MAPE, SD, and R-2 metrics. The comparison results indicated that the LSTM-PSO method provides highest results with values of R-2 (approximate to 0.9433), R-2 (approximate

A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates

River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kılayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazıköy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. 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 two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method’s performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model

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

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

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

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 uctepe 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 uctepe station and the whole Tuzla station. At uctepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m(3)/s, was 124.57 m(3)/s, and it was 184.06 m(3)/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R-2 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

A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series

River flow modeling plays a leading role in the management of water resources and ensuring sustainability. The complex nature of hydrological systems and the difficulty in the application process have led researchers to seek more instantaneous methods for flow predictions. Therefore, with the development of artificial intelligence-based techniques, hybrid modeling has become popular among hydrologists in recent years. For that reason, this study seeks to develop a hybrid model that integrates an adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) to predict river flow. Fundamentally, the performance of an ANFIS model depends on the optimum model parameters. Thus, it is aimed to increase the prediction performance by optimizing the ANFIS parameters with the population-based GA algorithm, which is a powerful algorithm. In this respect, the data gathered from Zamanti and Korkun Flow Measurement Stations (FMS) of Seyhan River, one of Turkey's significant rivers, were employed. Besides, the proposed hybrid ANFIS-GA approach was compared to classical ANFIS model to demonstrate the improvement of its performance. Also, within the scope of simulation studies, the traditional artificial neural networks (ANN) and the long-short term memory (LSTM) method which is a quite popular in recent years were used to predict streamflow data. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R-2 statistical metrics. In a nutshell, the outcomes indicated that the proposed ANFIS-GA method was the most successful model by achieving the highest values of R-2 (approximate to 0.9409) and R-2 (approximate to 0.9263) for the Zamanti and Korkun FMS data

A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates

River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kilayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazikoy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. 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 two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method's performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R-2 statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model

Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin

© 2023, The Author(s), under exclusive licence to Springer Nature B.V.The conditions which affect the sustainability of water cause a number of serious environmental and hydrological problems. Effective and correct management of water resources constitutes an effective and important issue among scales. In this sense, a precise estimation of streamflow time series in rivers is one of the most important issues in optimal management of surface water resources. Therefore, a hybrid method combining particle swarm algorithm (PSO) and long short-term memory networks (LSTM) are proposed to predict flow with data obtained from different flow measurement stations. In this respect, the data gathered from three Flow Measurement Stations (FMS) from Zamanti and Eğlence rivers located on Seyhan Basin are utilized. Besides, the proposed LSTM-PSO method is compared to an adaptive neuro-fuzzy inference system (ANFIS) and the LSTM benchmark model to demonstrate the performance achievement of proposed method. The prediction performances of the developed hybrid model and the others are tested on the determined stations. The forecasting performances of the models are determined with RMSE, MAE, MAPE, SD, and R2 metrics. The comparison results indicated that the LSTM-PSO method provides highest results with values of R2 (≈ 0.9433), R2 (≈ 0.6972), and R2 (≈ 0.9273) for the Değirmenocağı, Eğribük, and Ergenusagi FMS data, respectively