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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), Ada Boost 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 Suspended Sediment Load (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.

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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) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability has 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.

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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 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 (𝐶𝐶 𝑇𝑒𝑠𝑡𝑖𝑛𝑔 = 0.97, 𝑁𝑆𝐸 𝑇𝑒𝑠𝑡𝑖𝑛𝑔 = 0.948, 𝑅𝑀𝑆𝐸 𝑇𝑒𝑠𝑡𝑖𝑛𝑔 = 0.43 and 𝑀𝐴𝐸 𝑇𝑒𝑠𝑡𝑖𝑛𝑔 = 0.25) as the most accurate among the three models considered, highlighting the potential of Deep Learning approaches for water quality parameter prediction.