Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection

An intelligent approach for estimating aeration efficiency in stepped cascades: optimized support vector regression models and mutual information theory

Soft computing (SC) methods have increasingly been used to solve complex hydraulic engineering problems, especially those characterized by high uncertainty. SC approaches have previously proved to be an accurate tool for predicting the aeration efficiency coefficient (E20) in hydraulic structures such as weirs and flumes. In this study, the performance of the standalone support vector regression (SVR) algorithm and three of its hybrid versions, support vector regression–firefly algorithm (SVR-FA), support vector regression–grasshopper optimization algorithm (SVR-GOA), and support vector regression–artificial bee colony (SVR-ABC), is assessed for the prediction of E20 in stepped cascades. Mutual information theory is used to construct input variable combinations for prediction, including the parameters unit discharge (q), the total number of steps (N), step height (h), chute overall length (L), and chute inclination (α). Entropy indicators, such as maximum likelihood, Jeffrey, Laplace, Schurmann–Grassberger, and minimax, are computed to quantify the epistemic uncertainty associated with the models. Four indices—correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error (MAE)—are employed for evaluating the models’ prediction performance. The models’ outputs reveal that the SVR-FA model (with R=0.947,NSE=0.888,RMSE=0.048andMAE=0.027 in testing phase) has the best performance among all the models considered. The input variable combination, including q, N, h, and L, provides the best predictions with the SVR, SVR-FA, and SVR-GOA models. From the uncertainty analysis, the SVR-FA model shows the closest entropy values to the observed ones (3.630 vs. 3.628 for the “classic” entropy method and 3.647 vs. 3.643 on average for the Bayesian entropy method). This study proves that SC algorithms can be highly accurate in simulating aeration efficiency in stepped cascades and provide a valid alternative to the traditional empirical equation

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the implementation of a deep learning approach applied to a recurrent neural network (RNN) algorithm. The proposed deep recurrent neural network (DRNN) model is compared with support vector machine (SVM) and artificial neural network (ANN) models, formerly shown to be robust AI algorithms. The Fanno Creek in Oregon (USA) is selected as a case study and daily values of water temperature, specific conductance, streamflow discharge, pH, and DO concentration are used as input variables to predict DO concentration for three different lead times ("t + 1," "t + 3," and "t + 7"). Based on Pearson's correlation coefficient, several input variable combinations are formed and used for prediction. The model prediction performance is evaluated using various indices such as correlation coefficient, Nash-Sutcliffe efficiency, root mean square error, and mean absolute error. The results identify the DRNN model ([Formula: see text]) as the most accurate among the three models considered, highlighting the potential of deep learning approaches for water quality parameter prediction

Machine learning model development for predicting aeration efficiency through Parshall flume

This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression (RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSEtesting=0.002,R2testing=0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.Validerad;2021;Nivå 2;2021-06-07 (alebob)</p

Scouring Depth Assessment Downstream of Weirs Using Hybrid Intelligence Models

Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (RMSE = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (RMSE = 0.411)ANFIS-TLBO-M3 RMSEtesting=0.411, CCtesting~0.00) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability R-factor=1.72has a higher impact on the predicted results than the structure of models. In general, the ANFIS-IWO can be used as a reliable and cost-effective method for predicting the scouring depth downstream of weirs.Validerad;2020;Nivå 2;2020-06-15 (alebob)</p