River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables, e.g., precipitation, has a significant impact on the temporal distribution of the river discharge in recent days, making the discharge forecasting even more complicated for diversified water-related issues, e.g., flood prediction and irrigation planning. In order to predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to seven days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and R2 value of 0.93 with one day lead time. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

Data-driven flow forecasting models, such as Artificial Neural Networks (ANNs), are increasingly used for operational flood warning systems. In this research, we systematically evaluate different machine learning techniques (random forest and decision tree) and compare them with classical methods of the NAM rainfall run-off model for the Vésubie River, Nice, France. The modeled network is trained and tested using discharge, precipitation, temperature, and evapotranspiration data for about four years (2011–2014). A comparative investigation is executed to assess the performance of the model by using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and a correlation coefficient (R). According to the result, Feed Forward Neural Network (FFNN) (a type of ANN) models are less efficient than NAM models. The precision parameters correlation coefficient of ANN is 0.58 and for the NAM model is 0.76 for the validation dataset. In all machine learning models, the decision tree which performed best had a correlation coefficient of 0.99. ANN validation data prediction is good compared to the training, which is the opposite in the NAM model. ANN can be improved by fitting more input variables in the training dataset for a long period.

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; however, meeting the demand of high spatial extension and finer discretization and solving the physics-based numerical equations are computationally expensive. Computational efforts increase drastically with an increase in model dimension and resolution, preventing current solutions from fully realizing the data revolution. In this research, we demonstrate the effectiveness of artificial intelligence (AI), in particular, machine learning (ML) methods including the emerging deep learning (DL) to quantify urban flooding considering the lower part of Darby Creek, PA, USA. Training datasets comprise multiple geographic and urban hydraulic features (e.g., coordinates, elevation, water depth, flooded locations, discharge, average slope, and the impervious area within the contributing region, downstream distance from stormwater outfalls and dams). ML Classifiers such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN) are used to identify the flooded locations. A Deep neural network (DNN)-based regression model is used to quantify the water depth. The values of the evaluation matrices indicate satisfactory performance both for the classifiers and DNN model (F-1 scores- 0.975, 0.991, 0.892, and 0.855 for binary classifiers; root mean squared error- 0.027 for DNN regression). In addition, the blocked K-folds Cross Validation (CV) of ML classifiers in detecting flooded locations showed satisfactory performance with the average accuracy of 0.899, which validates the models to generalize to the unseen area. This approach is a significant step towards resolving the complexities of urban fluvial flooding with a large multi-dimensional dataset in a highly computationally efficient manner.

Distribution of the water flow path and residence time (HRT) in the hyporheic zone is a pivotal aspect in anatomizing the transport of environmental contaminants and the metabolic rates at the groundwater and surface water interface in fluvial habitats. Due to high variability in material distribution and composition in streambed and subsurface media, a pragmatic model setup in the laboratory is strenuous. Moreover, investigation of an individual streamline cannot be efficiently executed in laboratory experiments. However, an automated generation of water flow paths, i.e., streamlines in the hyporheic zone with a range of different streambed configurations could lead to a greater insight into the behavior of hyporheic water flow. An automated approach to quantifying the water flow in hyporheic zone is developed in this study where the surface water modeling tool, HER-RAS, and subsurface water flow modelling code, MIN3P, are coupled. A 1m long stream with constant water surface elevation of 2 cm to generate hydraulic head gradients and a saturated subsurface computational space with the dimensions of x:y:z = 1:0.1:0.1 m is considered to analyze the hyporheic exchange. Response in the hyporheic streamlines and residence time due to small-scale changes in the gravel-sand streambed were analyzed. The outcomes of the model show that the size, shape, and distribution of the gravel and sand portions have a significant influence on the hyporheic flow path and HRT. A high number and length of the hyporheic flow path are found in case of the highly elevated portion of gravel pieces. With the increase in the base width of gravel pieces, the length of hyporheic flow path and HRT decreases. In the case of increased amounts of gravel and sand portions on the streambed, both the quantity and length of the hyporheic flow path are reduced significantly.

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.