Predicting stable gravel-bed river hydraulic geometry: A test of novel, advanced, hybrid data mining algorithms

Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPS-IBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models, empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking

Difference in the bed load transport of graded and uniform sediments during floods: An experimental investigation

The objective of this study was to experimentally evaluate the difference in the transport of uniform (5.17, 10.35, 14, 20.7 mm) and graded sediment (mixture of these rounded particles with equal weight proportions) under different unsteady flow hydrographs in a 12 m long, 0.5 m wide and deep glass-walled flume. There was a lag time between fractions and uniform particles, such that peaks of coarser and finer fraction particles occurred before and after the peak of uniform sediment with the same size, respectively. Comparison between uniform particles and fractions in graded sediment showed that the sediment transport rate of fine and coarse fractions was lower and higher than their counterpart uniform particles, respectively. Overall, the uniform particles demonstrated a clockwise hysteresis loop and graded sediment had a counterclockwise hysteresis loop. The mobility of coarser fractions increased during the rising limb of hydrograph, whereas the mobility of finer fractions increased during the falling limb. In general, the mobility of coarse fractions increased and that of fine fractions reduced. Result of transported sediment showed that average particle size collected in traps (Db50) was coarser than bed material (Ds50) on both limbs. The relative transport ratio for uniform and graded sediment is higher and lower than 1, respectively

Experimental investigation of bed evolution resulting from dam break

This study examines the relations of structures and shapes of streambed evolution after dam break floods. A flume was used to simulate dam-break floods with variations of initial upstream water levels and variance, from uniform to graded, of the bed sediments. Detailed measurements of the evolution and composition were made during these experiments. The data indicate that intense scour occurred immediately downstream of the “dam break” in both uniform and graded sediments. The resulting bed surfaces of graded sediments showed coarse-fine-coarse structures in the areas with the lowest scour and highest deposition and various type of cluster (i.e. line and heap). This pattern was not observed in beds of uniform sediment. The scour hole changes from circular to oval-shaped in both uniform and graded sediments with increasing bed slopes. Keywords: Dam break, Experimental, Bed-surface composition, Graded and uniform sediment, Scour

Experimental Analysis of Incipient Motion for Uniform and Graded Sediments

So far, few studies have focused on the concept of critical flow velocity rather than bed shear stress for incipient sediment motion. Moreover, few studies have focused on sediment mixtures (graded sediment) and shape rather than uniform sediment for incipient motion condition. Different experiments were conducted at a hydraulic laboratory at the University of Guilan in 2015 to determine hydraulic parameters on the incipient motion condition. The aim of this study is to conduct a comparison between uniform and graded sediments, and a comparison between round and angular sediments. Experiments included rounded uniform bed sediments of 5.17, 10.35, 14 and 20.7 mm, angular uniform sediment of 10.35 mm, and graded sediment. Results demonstrated that angular sediment has a higher critical shear velocity than rounded sediment for incipient motion. Results also showed that for a given bed sediment, although critical shield stress and relative roughness increased with the bed slope, the particle Froude number (based on critical velocity) decreased. In terms of the sediment mixture, the critical shear stress (Vc*) was higher for the graded sediment than for the three finer uniform sediment sizes. The finer fractions of the mixture have a higher particle Froude number than their corresponding uniform sediment value, while the coarser fractions of the mixture showed a lower stability than their corresponding uniform sediment value. Results demonstrated that the reduction in the particle Froude number was more evident in lower relative roughness conditions. The current study provides a clearer insight into the interaction between initial sediment transport and flow characteristic, especially particle Froude number for incipient motion in natural rivers where stream beds have different gravel size distribution

Uniform and graded bed-load sediment transport in a degrading channel with nonequilibrium conditions

Bed-load transport plays a critical role in river morphological change and has an important impact on river ecology. Although there is good understanding of the role of the variation of river bed grain size on transport dynamics in equilibrium conditions, much less is understood for non-equilibrium conditions when the channel is either aggrading or degrading. In particular, the relative role of different grain sizes in the promotion and hindering of the transport of coarse and fine fractions in a degrading channel has yet to be investigated. The current study attempts to provide new understanding through a series of flume experiments done using uniform and graded sediment particles. The experiments revealed coarser grain-size fractions for a poorly sorted sediment, relative to uniform-sized sediment, reduced the transport of finer grains and finer fractions enhanced the transport of coarse grains. This hindering-promotion effect, caused by relative hiding and exposure of finer and coarse fractions, increased with bed slope and decreased with relative submergence. In particular, as relative submergence increased, the graded fractions tended towards behaving more like their uniform-sized counterparts. Also, the bed-load parameter of the graded fractions increased more with a rise in bed slope than observed for the uniform-sized counterparts. These results revealed, for degrading channel conditions, such as downstream of a dam, bed-load equations developed for uniform bed sediment are inappropriate for use in natural river systems, particularly in mountain streams. Furthermore, changes in river bed composition due to activities that enhance the input of hill-slope sediment, such as fire, logging, and agricultural development, are likely to cause significant changes in river morphology

Cumulative Infiltration and Infiltration Rate Prediction Using Optimized Deep Learning Algorithms: A Study in Western Iran

Study region: Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focus: Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. New Hydrological Insights for the Region: Finding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates