River Sediment Amounts Prediction with Regression and Support Vector Machine Methods.

Accurate estimation of the amount of sediment in rivers; determination of pollution, river transport, determination of dam life, etc. matters are very important. In this study, sediment estimation in the river was made using Interaction Regression (IR), Pure-Quadratic Regression (PQR) and Support Vector machine (SVM) methods. The observation station on the Patapsco River near Catonsville was chosen as the study area. Prediction model was developed by using daily flow and turbidity data between 2015- 2018 as input parameters. Models were compared to each other according to three statistical criteria, namely, root mean square errors (RMSE), mean absolute relative error (MAE) and determination coefficient (R2 ). These criteria were used to evaluate the performance of the models. When the model results were compared with each other, it was seen that the IR model gave results consistent with the actual measurement results

Prediction of the Dissolved Oxygen by Using Multi-Layer Perceptron and KNN Approaches: A Case Study in Coosa River, Alabama, USA

The dissolved oxygen amount of a water body, such as a reservoir, stream, or river, is an important water quality parameter that may affect society's health directly. The daily mean dissolved oxygen of the Coosa River was investigated in this presented study. The multi-layer Perceptron (MLP) approach and k-nearest neighbor (KNN) algorithm, recently widely used for hydrological and environmental problems, was chosen for the prediction. Daily water temperature (Max, Min, and Mean), daily mean specific conductivity, daily median water pH, and discharge parameters were inputs in the MLP and KNN models. A total of 3535 daily records were implemented into the model. 2951 daily data were used as the training set, while 584 was the test set. Results were compared with each other by using statistical evaluation methods. The KNN approach was also generated by applying the same training and test sets. Based on the results, it is evident that the MLP (Multilayer Perceptron) model provided satisfactory dissolved oxygen prediction results. However, the KNN (K-Nearest Neighbors) model outperformed the MLP approach, despite having a lower correlation coefficient than the MLP

Suspended Sediment Estimation Using Machine Learning Methods

Suspended sediment in rivers is important for efficiently using water resources and hydraulic structures. In this study, the suspended sediment load of rivers was estimated using traditional multi-linear regression (MLR), machine learning methods such as the support vector machines (SVM) and M5 decision tree (M5T). Data on daily stream flow, daily maximum and minimum water temperature and suspended sediment concentration in the river were used as input data in all models to predict daily suspended sediment discharge. The performance of all methods is evaluated based on a statistical approach. Determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) are used as comparison criteria. Overall, the machine learning approaches better predict suspended sediment discharge

Numerical Analysis of the Flow Over the Dam Spillway

The determination of hydraulic parameters is very important in the design of the dam spillway structure. Hydraulic parameters are obtained by theoretical and empirical approaches according to the design flow discharge. In general, before the application project, tests are made on the hydraulic model and the design is given its final shape. Advanced numerical modeling techniques can be used in conjunction with or as an alternative to experimental studies. This study investigated hydraulic parameters in three dimensions under design flow in a dam spillway using computational fluid dynamics (CFD). In the numerical model, the VOF method, which can solve two-phase flows, and the standard k-e turbulence model are used. Obtained results were compared with experimental results. It was determined that the experimental and numerical model results were quite compatible with each other

Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach

Suspended sediment estimation is important to the water resources management and water quality problem. In this article, artificial neural networks (ANN), M5tree (M5T) approaches and statistical approaches such as Multiple Linear Regression (MLR), Sediment Rating Curves (SRC) are used for estimation daily suspended sediment concentration from daily temperature of water and streamflow in river. These daily datas were measured at Iowa station in US. These prediction aproaches are compared to each other according to three statistical criteria, namely, mean square errors (MSE), mean absolute relative error (MAE) and correlation coefficient (R). When the results are compared ANN approach have better forecasts suspended sediment than the other estimation methods

Numerical Modeling of Submerged Vane Flow

Scours in rivers occur due to high flow velocities. In order to reduce scour, flow velocities need to be reduced. Submerged vane structures are effective in both reducing the flow rate and directing the flow. In this study, numerical modeling was made with submerged vane structures. Models of the measured flow velocities in the channel, where submerged vane experiments were performed before, were compared with the results of the submerged vane experiment by using the 3-dimensional computational fluid dynamics (CFD) method. In the present CFD model, continuity and momentum, turbulence model equations are applied. For the turbulence viscosity, k-ε turbulence model is used. The results of the present model are compared with the previous experimental work

Experimental and Numerical Study on Flow Control Using 3-Array Submerged Vane in Laboratory Channel Bend

Regulation structures such as submerged vane are needed to reduce and eliminate environmental damage due to increased flooding in rivers. In particular, scours on the outer bank due to increased flow velocities cause the river bed to change and deteriorate. In this study, the effect on flow velocities was investigated experimentally by using 3-array submerged vane structures in areas close to the outer bank. The experimental vane results were performed in the open channel setup. The Computational Fluid Dynamics (CFD) results obtained with the numerical model were also verified and compared with experimental results. It has been observed that the CFD model gives results close to the real experimental results. The standard-based k-ε model was used as the turbulence model. In the outer meander, the 3-array submerged vane with a 3-vane structure was found to affect the flow velocity by 16–27% in the region behind the vane. The flow velocities were investigated along with depth using the CFD and found that the mean velocity was reduced by 14–21% along the depth. It is also recommended that submerged vane structures can be applied as an effective method in reducing flow velocities and directing flows

Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques

Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values

Flood Hydraulic Analyses: A Case Study of Amik Plain, Turkey

In recent years, significant flood events have occurred in various parts of the world. The most important reasons for these events are global warming and consequent imbalances in climate and rainfall regimes. Many studies are performed to prevent the loss of life and property caused by floods. Many methods have been developed to predict future floods and possible affected areas. Developing computer and numerical calculation methods gives opportunities to make simulations of flood hazards. One of the affected areas, which is also one of the world’s first residential districts at Hatay in Turkey, is the Amik Plain. In this study, the floods on the Amik Plain in Hatay province are analyzed. Hatay airport was also affected during floods since 2012 and serious material damage occurred. For this purpose, Google Earth Pro software was used to obtain maps of the basin where the airport is located and the rivers it contains. Afterwards, Hydrologic Engineering Center’s River Analysis System module (HEC-RAS) was used for the hydraulic and hydrological definitions of the river basin. The results of numerical models are presented as simulated maps