Improving the accuracy of a remotely-sensed flood warning system using a multi-objective pre-processing method for signal defects detection and elimination

One of the primary goals of watershed management is to proactively monitor and forecast flood water levels to provide early warning for timely evacuation plans and save lives. One of the most economical ways to accomplish this objective is to use remotely-sensed satellite signals. Previous studies have indicated that an Advanced Microwave Scanning Radiometer (AMSR) sensor can be used for river water level monitoring combined with a few in-situ hydrometric gauges for the ground-truth data collection. However, space-based signals are influnced by many error-inducing natural factors, such as dust and cloud cover. Hence, a hybrid method is proposed, which comprises of a multi-objective particle swarm optimization model, a decision tree classification algorithm, the Hotelling’s $T^{2}$ outlier detection, and a regression model to identify and replace inaccurate space-based signals. This complex hybrid method will be referred to, in this study, with the acronym (OCOR). In the first phase of this hybrid method, the outlier signals are detected and eliminated from the dataset, and in the second phase, the eliminated signals along with signals lost due to satellite technical problems are estimated by ground-truth data calibration using in situ hydrometric stations. The two case studies of the White and Willamette Rivers demonstrate the performance of OCOR in practical situations

Improving the accuracy of a remotely-sensed flood warning system using a multi-objective pre-processing method for signal defects detection and elimination

One of the primary goals of watershed management is to proactively monitor and forecast flood water levels to provide early warning for timely evacuation plans and save lives. One of the most economical ways to accomplish this objective is to use remotely-sensed satellite signals. Previous studies have indicated that an Advanced Microwave Scanning Radiometer (AMSR) sensor can be used for river water level monitoring combined with a few in-situ hydrometric gauges for the ground-truth data collection. However, space-based signals are influnced by many error-inducing natural factors, such as dust and cloud cover. Hence, a hybrid method is proposed, which comprises of a multi-objective particle swarm optimization model, a decision tree classification algorithm, the Hotelling’s $T^{2}$ outlier detection, and a regression model to identify and replace inaccurate space-based signals. This complex hybrid method will be referred to, in this study, with the acronym (OCOR). In the first phase of this hybrid method, the outlier signals are detected and eliminated from the dataset, and in the second phase, the eliminated signals along with signals lost due to satellite technical problems are estimated by ground-truth data calibration using in situ hydrometric stations. The two case studies of the White and Willamette Rivers demonstrate the performance of OCOR in practical situations

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equationsdo not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = –0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided

New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

Abstract In many hydraulic structures, side weirs have a critical role. Accurately predicting the discharge coefficient is one of the most important stages in the side weir design process. In the present paper, a new high efficient side weir is investigated. To simulate the discharge coefficient of these side weirs, three novel soft computing methods are used. The process includes modeling the discharge coefficient with the hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) and three optimization algorithms, namely Differential Evaluation (ANFIS-DE), Genetic Algorithm (ANFIS-GA) and Particle Swarm Optimization (ANFIS-PSO). In addition, sensitivity analysis is done to find the most efficient input variables for modeling the discharge coefficient of these types of side weirs. According to the results, the ANFIS method has higher performance when using simpler input variables. In addition, the ANFIS-DE with RMSE of 0.077 has higher performance than the ANFIS-GA and ANFIS-PSO methods with RMSE of 0.079 and 0.096, respectively

Standard equations for predicting the discharge coefficient of a modified high-performance side weir

Side weirs are hydraulic structures that are used as discharge adjustments to divert the surplus water flowing from the main channel. Predicting the discharge coefficient is one of the most important parameters in the side weir design process. In practical situations, it is preferred to predict the discharge coefficient with simple equations. The goal of this study is to develop accurate standard equations for use in predicting the discharge coefficient of a high-performance, modified triangular side weir. The Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the equations. Four different forms of the equations and two non-dimensional input combinations were used to develop the most appropriate model. The results obtained by our simple standard equations optimized by the PSO algorithm were compared with those of complex nonlinear regression equations, and our equations were more accurate in modeling the discharge coefficient. Our method reduced the error in the results by as much as 43% compared to the regression methods, and its simplicity makes it useful in solving practical problems

A new hybrid decision tree method based on two artificial neural networks for predicting sediment transport in clean pipes

A new hybrid decision tree (DT) technique based on two artificial neural networks (ANN), namely multilayer perceptron (MLP) and radial basis function (RBF), is proposed to predict sediment transport in clean pipes (i.e. without deposition). The parameters affecting densimetric Froude number (Fr) prediction were extracted from the literature in order to build the model proposed in this study. The effect of each parameter is first examined using MLP and RBF and a sensitivity analysis. According to the sensitivity analysis, the optimal model indicates that using the volumetric sediment concentration (CV), median diameter of particle size distribution to pipe diameter (d/D) and ratio of median diameter of particle size distribution to hydraulic radius (d/R) parameters yield the best Fr prediction results. Subsequently, the hybrid DT-MLP and DT-RBF model results are compared with MLP and RBF. According to the results, MLP with all models predicted Fr more accurately than RBF, and DT-MLP exhibited the best performance (R2 = 0.975, MARE = 0.063, RMSE = 0.328, SI = 00.081, BIAS = −0.01). Moreover, the comparison between DT-MLP and existing regression-based equations indicates that the models presented in the current study are superior. Keywords: Artificial Neural Network (ANN), Decision Tree (DT), Hybrid model, Multilayer Perceptron (MLP), Radial Basis Function (RBF), Sediment transpor

GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs

Estimating the discharge coefficient using hydraulic and geometrical specifications is one of the influential factors in predicting the discharge passing over a side weir. Taking into account the fact that existing equations are incapable of estimating the discharge coefficient well, artificial intelligence methods are used to predict it. In this study, Group Method of Data Handling (GMDH) was used for the purpose of predicting the discharge coefficient in a side weir. The Froude number (F1), weir dimensionless length (b/B), ratios of weir length to depth of upstream flow (b/y1) and weir height to its length (p/y1) were taken as input parameters to express a new model for predicting the discharge coefficient. Two different sets of laboratory data were used to train the artificial network and test the new model. Different statistical indexes were used to evaluate the performance of the GMDH model presented for two states, training and testing. The results indicate that the proposed model predicts the discharge coefficient precisely (MAPE = 5.263 & RMSE = 0.038) and this model is more accurate in predicting than the feed-forward neural network model and existing nonlinear regression equations

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided

Design of modified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in 90° open-channel bends

A modified multi-layer perceptron (MLP) model based on decision trees (DT-MLP) is presented to predict velocity and water free-surface profiles in a 90° open-channel bend. The ability of the new hybrid model to predict the velocity and flow depth in a 90° sharp bend is investigated and compared with the abilities of MLP and multiple-linear regression (MLR) models. The MLP and DT-MLP networks are trained and tested using 520 and 506 experimental data measured for velocity and flow depth, respectively, at five different discharge rates of 5, 7.8, 13.6, 19.1 and 25.3 l/s. The MLP and DT-MLP comparison results against MLR reveal that the two artificial neural networks (ANNs) are 84% and 16% more accurate than the MLR model in predicting the velocity and flow depth variables, respectively. According to the results, the root mean square error (RMSE) value of the DT-MLP model decreases by 9% and 7.5% in predicting velocity and flow depth, respectively, compared with the MLP model. It was found that the hybrid decision-tree-based method can significantly improve MLP neural network performance in forecasting velocity and free-surface profiles in a 90° open-channel bend