Prediction of channel sinuosity in perennial rivers using Bayesian Mutual Information theory and support vector regression coupled with meta-heuristic algorithms

Support Vector Regression (SVR) combined with Invasive Weeds Optimization (IWO), standalone SVR, and Radial Basis Function Neural Networks are applied to estimate channel sinuosity in perennial rivers. With this aim, a dataset with 132 sinuosity data and related geomorphologic data, corresponding to 119 perennial streams, is considered. Bayesian Mutual Information theory is used to determine the parameters affecting channel sinuosity to reveal that bankfull depth affects sinuosity the most. Seven input parameter combinations for sinuosity prediction are considered, and in both training and testing stages, the SVR-IWO model (RTrain=0.959,RMSETrain=0.072,MAETrain=0.037,Rtest=0.892,RMSETest=0.103,MAETest=0.065) shows the best prediction performance while the standalone SVR model generated the results with performances of (RTrain=0.792,RMSETrain=0.158,MAETrain=0.141,Rtest=0.704,RMSETest=0.163,MAETest=0.151). Model prediction uncertainty is quantified in terms of entropy for the three models considered, further confirming that the sinuosity set predicted by the SVR-IWO model is the closest to the observed set

The Application of Soft Computing Models and Empirical Formulations for Hydraulic Structure Scouring Depth Simulation: A Comprehensive Review, Assessment and Possible Future Research Direction

Prediction of scouring characteristics is one of the major issues in hydraulic and hydrology engineering. Over the past five decades, numerous empirical formulations (EFs), based on the regression of scouring data observed from laboratory experiments in the field, have been developed to predict scouring characteristics (typically, the equilibrium scour depth); yet, these EFs are sensitive to uncertainty of effective parameters and in some cases could not comprehend the actual internal mechanism between variables. In the last 20 years, Soft Computing (SC) approaches have been increasingly adopted as an alternative for modeling scouring depth surrounding hydraulic structures. In this respect, several SC algorithms are examined as new era of modeling methodologies for extracting scouring depth equations. Lately, these algorithms have been vastly adopted for scouring simulation with various advanced version of SC such as hybrid intelligence models. The motivation of the current research is to exhibit all the established researches on the implementation of EF and SC models for multiple scouring depth modeling such as around pipeline, bridges abutment, piles and grade-control structures. A comprehensive review of the up-to-date researches on the scouring depth phenomena is presented, placing special emphasis on the recent applications of SC models and also recalling all the performed experimental laboratory studies. The review is included an informative evaluation and assessment of the surveyed researches. The improvement in prediction performance provided by the SC models when compared to empirical formulations is discussed and based on the current state-of-the-art, several research gaps are recognized, and possible future research directions are proposed

Applications of soft computing models for predicting sea surface temperature : a comprehensive review and assessment

The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.Validerad;2021;Nivå 2;2021-01-11 (alebob)</p

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

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

Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways

Development of Advanced Computer Aid Model for Shear Strength of Concrete Slender Beam Prediction

High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.Validerad;2020;Nivå 2;2020-06-17 (alebob)</p