Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model

Streamflow forecasting is vital for managing water resources, such as flood control, agriculture planning, hydropower generation, environmental management, drought management, and water quality management. Motivated by the success of artificial intelligence models for hydrological applications, this...

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Veröffentlicht in:	Water resources management Jg. 38; H. 15; S. 5973 - 5989
Hauptverfasser:	Kumshe, Umar Muhammad Mustapha, Abdulhamid, Zakariya Muhammad, Mala, Baba Ahmad, Muazu, Tasiu, Muhammad, Abdullahi Uwaisu, Sangary, Ousmane, Ba, Abdoul Fatakhou, Tijjani, Sani, Adam, Jibril Muhammad, Ali, Mosaad Ali Hussein, Bello, Aliyu Uthman, Bala, Muhammad Muhammad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.12.2024 Springer Nature B.V
Schlagworte:	Artificial intelligence Artificial neural networks Atmospheric Sciences Civil Engineering data collection Datasets Drought Earth and Environmental Science Earth Sciences Environment Environmental management Flood control Flood forecasting Flood management Geotechnical Engineering & Applied Earth Sciences Hydroelectric power Hydroelectric power generation Hydrogeology Hydrology/Water Resources Long short-term memory Neural networks Performance evaluation Quality management Root-mean-square errors Statistical models Stream discharge Stream flow Streamflow forecasting water Water management water power Water quality Water quality management Water resources Water resources management Deep learning Hydrology LSTM Ala river Autoencoder Streamflow
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
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Abstract	Streamflow forecasting is vital for managing water resources, such as flood control, agriculture planning, hydropower generation, environmental management, drought management, and water quality management. Motivated by the success of artificial intelligence models for hydrological applications, this study proposes a model that integrates an autoencoder, the Convolutional Neural Networks (CNN), and the Long Short Term Memory (LSTM) networks. Thirty years daily dataset were served to the Autoencoder Convolutional Neural Network Long Short Term Memory (AE-CNN-LSTM) and the baseline models. To evaluate the model's accuracy, 80% of the dataset was used for training and the remaining 20% was used to test the performance of these models. Statistical metrics, for instance, the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), the Nash–Sutcliffe Efficiency (NSE), and the Coefficient of determination (R 2 ) were employed to evaluate the model’s performance. In terms of train RMSE, test RMSE, train MAE, test MAE, train MAPE, test MAPE, train NSE, test NSE, train R 2 , and test R 2 , the proposed model significantly obtained the best results with values of 2.6299, 2.7971, 0.1676, 0.1881, 16.76, 18.81, 0.98, 0.97, 0.98, and 0.96, respectively.
AbstractList	Streamflow forecasting is vital for managing water resources, such as flood control, agriculture planning, hydropower generation, environmental management, drought management, and water quality management. Motivated by the success of artificial intelligence models for hydrological applications, this study proposes a model that integrates an autoencoder, the Convolutional Neural Networks (CNN), and the Long Short Term Memory (LSTM) networks. Thirty years daily dataset were served to the Autoencoder Convolutional Neural Network Long Short Term Memory (AE-CNN-LSTM) and the baseline models. To evaluate the model's accuracy, 80% of the dataset was used for training and the remaining 20% was used to test the performance of these models. Statistical metrics, for instance, the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), the Nash–Sutcliffe Efficiency (NSE), and the Coefficient of determination (R²) were employed to evaluate the model’s performance. In terms of train RMSE, test RMSE, train MAE, test MAE, train MAPE, test MAPE, train NSE, test NSE, train R², and test R², the proposed model significantly obtained the best results with values of 2.6299, 2.7971, 0.1676, 0.1881, 16.76, 18.81, 0.98, 0.97, 0.98, and 0.96, respectively. Streamflow forecasting is vital for managing water resources, such as flood control, agriculture planning, hydropower generation, environmental management, drought management, and water quality management. Motivated by the success of artificial intelligence models for hydrological applications, this study proposes a model that integrates an autoencoder, the Convolutional Neural Networks (CNN), and the Long Short Term Memory (LSTM) networks. Thirty years daily dataset were served to the Autoencoder Convolutional Neural Network Long Short Term Memory (AE-CNN-LSTM) and the baseline models. To evaluate the model's accuracy, 80% of the dataset was used for training and the remaining 20% was used to test the performance of these models. Statistical metrics, for instance, the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), the Nash–Sutcliffe Efficiency (NSE), and the Coefficient of determination (R 2 ) were employed to evaluate the model’s performance. In terms of train RMSE, test RMSE, train MAE, test MAE, train MAPE, test MAPE, train NSE, test NSE, train R 2 , and test R 2 , the proposed model significantly obtained the best results with values of 2.6299, 2.7971, 0.1676, 0.1881, 16.76, 18.81, 0.98, 0.97, 0.98, and 0.96, respectively. Streamflow forecasting is vital for managing water resources, such as flood control, agriculture planning, hydropower generation, environmental management, drought management, and water quality management. Motivated by the success of artificial intelligence models for hydrological applications, this study proposes a model that integrates an autoencoder, the Convolutional Neural Networks (CNN), and the Long Short Term Memory (LSTM) networks. Thirty years daily dataset were served to the Autoencoder Convolutional Neural Network Long Short Term Memory (AE-CNN-LSTM) and the baseline models. To evaluate the model's accuracy, 80% of the dataset was used for training and the remaining 20% was used to test the performance of these models. Statistical metrics, for instance, the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), the Nash–Sutcliffe Efficiency (NSE), and the Coefficient of determination (R2) were employed to evaluate the model’s performance. In terms of train RMSE, test RMSE, train MAE, test MAE, train MAPE, test MAPE, train NSE, test NSE, train R2, and test R2, the proposed model significantly obtained the best results with values of 2.6299, 2.7971, 0.1676, 0.1881, 16.76, 18.81, 0.98, 0.97, 0.98, and 0.96, respectively.
Author	Sangary, Ousmane Mala, Baba Ahmad Ali, Mosaad Ali Hussein Adam, Jibril Muhammad Muazu, Tasiu Bala, Muhammad Muhammad Muhammad, Abdullahi Uwaisu Tijjani, Sani Ba, Abdoul Fatakhou Bello, Aliyu Uthman Kumshe, Umar Muhammad Mustapha Abdulhamid, Zakariya Muhammad
Author_xml	– sequence: 1 givenname: Umar Muhammad Mustapha surname: Kumshe fullname: Kumshe, Umar Muhammad Mustapha organization: College of Computer and Information, Hohai University – sequence: 2 givenname: Zakariya Muhammad surname: Abdulhamid fullname: Abdulhamid, Zakariya Muhammad organization: Software College, Northeastern University – sequence: 3 givenname: Baba Ahmad surname: Mala fullname: Mala, Baba Ahmad organization: School of Information and Communication Engineering, Huazhong University of Science and Technology – sequence: 4 givenname: Tasiu surname: Muazu fullname: Muazu, Tasiu organization: College of Computer and Information, Hohai University – sequence: 5 givenname: Abdullahi Uwaisu surname: Muhammad fullname: Muhammad, Abdullahi Uwaisu email: ma.uwais@fud.edu.ng organization: College of Computer and Information, Hohai University, Department of Computer Science, Federal University Dutse – sequence: 6 givenname: Ousmane surname: Sangary fullname: Sangary, Ousmane organization: School of Computer Science, Hubei University of Technology – sequence: 7 givenname: Abdoul Fatakhou surname: Ba fullname: Ba, Abdoul Fatakhou organization: College of Computer and Information, Hohai University – sequence: 8 givenname: Sani surname: Tijjani fullname: Tijjani, Sani organization: Computer Engineering Department, Kano State Polytechnic – sequence: 9 givenname: Jibril Muhammad surname: Adam fullname: Adam, Jibril Muhammad organization: Department of Computer Science, Federal University Dutse – sequence: 10 givenname: Mosaad Ali Hussein surname: Ali fullname: Ali, Mosaad Ali Hussein organization: Mining and Metallurgical Engineering Department, Assiut University – sequence: 11 givenname: Aliyu Uthman surname: Bello fullname: Bello, Aliyu Uthman organization: Department of Information Technology, Federal University Dutse – sequence: 12 givenname: Muhammad Muhammad surname: Bala fullname: Bala, Muhammad Muhammad organization: Department of Computer Science, Kano University of Science and Technology
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SubjectTerms	Artificial intelligence Artificial neural networks Atmospheric Sciences Civil Engineering data collection Datasets Drought Earth and Environmental Science Earth Sciences Environment Environmental management Flood control Flood forecasting Flood management Geotechnical Engineering & Applied Earth Sciences Hydroelectric power Hydroelectric power generation Hydrogeology Hydrology/Water Resources Long short-term memory Neural networks Performance evaluation Quality management Root-mean-square errors Statistical models Stream discharge Stream flow Streamflow forecasting water Water management water power Water quality Water quality management Water resources Water resources management
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Title	Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model
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