Publication: Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
Date
2023
Authors
Latif S.D.
Ahmed A.N.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media B.V.
Abstract
As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources
they have benefited human cultures in a variety of ways, including enhanced human health, increased food production, water supply for domestic and industrial use, economic growth, irrigation, hydro-power generation, and flood control. This study aims to compare the application of deep learning and conventional machine learning algorithms for predicting daily reservoir inflow. Long short-term memory (LSTM) has been applied as a deep learning algorithm and boosted regression tree (BRT) has been implemented as a machine learning algorithm. Five statistical indices have been selected to evaluate the performance of the proposed models. The selected statistical measurements are mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). The findings showed that LSTM outperformed BRT with a significant difference in terms of accuracy. � 2023, The Author(s), under exclusive licence to Springer Nature B.V.
they have benefited human cultures in a variety of ways, including enhanced human health, increased food production, water supply for domestic and industrial use, economic growth, irrigation, hydro-power generation, and flood control. This study aims to compare the application of deep learning and conventional machine learning algorithms for predicting daily reservoir inflow. Long short-term memory (LSTM) has been applied as a deep learning algorithm and boosted regression tree (BRT) has been implemented as a machine learning algorithm. Five statistical indices have been selected to evaluate the performance of the proposed models. The selected statistical measurements are mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). The findings showed that LSTM outperformed BRT with a significant difference in terms of accuracy. � 2023, The Author(s), under exclusive licence to Springer Nature B.V.
Description
Keywords
Boosted regression tree (BRT) , Dokan dam , Long short-term memory (LSTM) , Reservoir inflow , Brain , Climate change , Economics , Errors , Flood control , Learning algorithms , Learning systems , Mean square error , Reservoirs (water) , Water conservation , Water management , Water supply , Boosted regression tree , Boosted regression trees , Dokan dam , Long short-term memory , Machine learning algorithms , Reservoir inflow , Root mean square errors , Streamflow prediction , Sustainable water supply , Water supply management , regression analysis , reservoir , sustainable development , water management , water resource , water supply , Long short-term memory