Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

• Published in: Mathematics (Basel) Vol. 10; no. 16; p. 2971
• Main Authors: Ikram, Rana Muhammad Adnan, Goliatt, Leonardo, Kisi, Ozgur, Trajkovic, Slavisa, Shahid, Shamsuddin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Adaptation; Algorithms; Analysis; Analysis of covariance; Artificial neural networks; Covariance matrix; covariance matrix adaptation evolution strategy; elastic net; Errors; Evolution; extreme learning machine; Food science; Gaussian process; Heuristic; Hydroelectric power; Hydrology; Innovations; Machine learning; Matrices; Neural networks; Numerical prediction; Optimization; Precipitation; Prediction theory; Radial basis function; Statistical analysis; Strategy; Stream flow; Streamflow; streamflow prediction; Support vector machines; support vector regression; Uncertainty analysis; Water resources; Water shortages; Brazil; Turkey; Pakistan; United States > US; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10162971

Precise streamflow estimation plays a key role in optimal water resource use, reservoirs operations, and designing and planning future hydropower projects. Machine learning models were successfully utilized to estimate streamflow in recent years In this study, a new approach, covariance matrix adaptation evolution strategy (CMAES), was utilized to improve the accuracy of seven machine learning models, namely extreme learning machine (ELM), elastic net (EN), Gaussian processes regression (GPR), support vector regression (SVR), least square SVR (LSSVR), extreme gradient boosting (XGB), and radial basis function neural network (RBFNN), in predicting streamflow. The CMAES was used for proper tuning of control parameters of these selected machine learning models. Seven input combinations were decided to estimate streamflow based on previous lagged temperature and streamflow data values. For numerical prediction accuracy comparison of these machine learning models, six statistical indexes are used, i.e., relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), and the Kling–Gupta efficiency agreement index (KGE). In contrast, this study uses scatter plots, radar charts, and Taylor diagrams for graphically predicted accuracy comparison. Results show that SVR provided more accurate results than the other methods, especially for the temperature input cases. In contrast, in some streamflow input cases, the LSSVR and GPR were better than the SVR. The SVR tuned by CMAES with temperature and streamflow inputs produced the least RRMSE (0.266), MAE (263.44), and MAPE (12.44) in streamflow estimation. The EN method was found to be the worst model in streamflow prediction. Uncertainty analysis also endorsed the superiority of the SVR over other machine learning methods by having low uncertainty values. Overall, the SVR model based on either temperature or streamflow as inputs, tuned by CMAES, is highly recommended for streamflow estimation.