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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Bibliographic Details
Published in:Water resources management Vol. 38; no. 15; pp. 5973 - 5989
Main Authors: 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
Language:English
Published: Dordrecht Springer Netherlands 01.12.2024
Springer Nature B.V
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-024-03937-2