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

Abstract

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.

Hybridized Adaptive Neuro-Fuzzy Inference System with Metaheuristic Algorithms for Modeling Monthly Pan Evaporation

“…Many studies have demonstrated that WOA not only minimizes the cost of solving engineering problems, but also provides better optimization efficiency compared to other population-based metaheuristic algorithms [45,46]. Overall, our results demonstrated that the hybridized models reduced the drawbacks of the standalone ANFIS and enabled a more accurate model Epan, in line with previous studies [14,21,23,31,32,34].…”

“…Therefore, automatic parameter tuning using metaheuristic optimization algorithms has received attention from many researchers studying real-world problems. Examples of the metaheuristic optimization algorithms used for tuning a base method, such as ANN, LSSVM, SVR and ANFIS [23,24], include an electrostatic discharge algorithm [25], a water cycle optimization algorithm (WCA) [26], atom search optimization (ASO) [27], particle swarm optimization (PSO) [28], a cultural algorithm (CA) [29], a bee colony algorithm (BCA) [30], a genetic algorithm (GA) [31], biogeography-based optimization [24], and a firefly algorithm (FFA) [32]. Table 1 compares the hybridized ANFIS models with other ML methods for modeling evaporation as used in previous studies.…”

Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods

“…Additionally, it excels at analyzing nonlinear datasets by utilizing probabilistic approaches and approximating posterior degradation by requiring prior data distribution. The diversity of the covariance function is an essential component of the Gaussian process, which helps to develop tasks with different structures [44][45][46]. Also, the standard property of the GPR model makes this model vital for statistical modeling.…”

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

“…Nonetheless, using statistically based methods to solve a highly non-linear issue such as flyrock and rock fragmentation can be a challenging and difficult endeavor. Many attempts are conducted to solve engineering problems by using artificial intelligence and soft computing techniques [16][17][18][19][20][21][22][23][24][25]. Therefore, the application of intelligent machine learning, such as artificial intelligence (AI) and soft computing (SC), could have relevance and benefit when attempting to solve issues related to this type.…”