Search

Evaluation of hydraulic parameters in an open channel plays a pivotal role in the design and estimation of different hydraulic characteristics; therefore, an experimental-based research study was conducted on different canals tertiary in the command of three distributaries falling in the hydraulic boundaries of the Jamrao canal. Keeping in the view objectives of the study, the recorded data of hydraulic characteristics (discharge, wetted perimeter, roughness coefficient, and bed slope) were determined and correlated using simple linear regression analysis for earthen watercourses to develop the empirical equations for important hydraulic characteristics. An empirical equation for the roughness coefficient has developed which is simple and explicit for computing the value of the roughness coefficient as compared to Universal Manning's formula. The comparison of observed values of important hydraulic characteristics and roughness coefficient computed from empirical equations for the watercourse has yielded satisfactory results. The mean absolute error of important hydraulic parameters viz. discharge, wetted perimeter, roughness coefficient, and bed slope is in the order of ±10.23%, ±4.25%, ±9.9%, and ±3.2% respectively. The value of the coefficient of determination (R2) for the set of these equations is within the range of 78 to 96% which is the indicator of an excellent correlation within the variables. The general empirical equations developed from this study can fairly be used in determining the flow variables particularly discharge in the tertiary canals without using a cumbersome method that is a cut-throat flume and associated monographs. [ABSTRACT FROM AUTHOR]

This study compares samples of erosion resistant coatings made of geomat filled with crushed stone with bituminous and bitumen-polymer binders. The comparison was made several years after the production of the coatings under study. The article provides conclusions about the coating durability and the specific features of its application, as well as recommendations for using roughness coefficient. [ABSTRACT FROM AUTHOR]

Calibrating hydraulic models for water distribution systems (WDS) is crucial during model-building, particularly in determining the roughness coefficients of pipes. However, using a single roughness coefficient based solely on pipe material can lead to significant variations in frictional head losses. To address this issue and enhance computational efficiency, a genetic algorithm (GA) for optimizing roughness coefficients is presented With the Environmental Protection Agency Network Evaluation Tool (EPANET) hydraulic model. EPANET-GA further considers the spatial characteristics of pipes. We incorporated an automated calibration process and a user graphic interface to analyze the water head pressures of WDS nodes for the Zhonghe-Yonghe Division. The results reveal that the optimized roughness coefficient produces a high correlation coefficient (0.90) with the measured data in a time slot. In addition, a low standard error (8.93%) was achieved for 24-hour predictions. Furthermore, in the Shelin-Beitou Division, spatial characteristics were incorporated as constraints during the calibration process. The EPANET-GA has the potential to serve as an excellent tool for designing, operating, and optimizing water supply networks. It can become an advanced operational solution for administrations, aiding in tasks such as leakage detection and pump energy optimization. [ABSTRACT FROM AUTHOR]

Soil–rock mixture (S–RM) deposits are encountered quite often in most of the landslide sites, especially in the lower Shivalik region of the Indian Himalayas. The assessment of shear strength of such deposits is difficult due to complex interaction of rock particles with sand fines matrix. Limited studies are available in the literature which deals with shear strength response of S–RM. As a result, very few approaches are available in the literature that can be applied to assess the shear strength of S–RM. The present study makes an attempt in the direction of solving this problem through an extensive laboratory study. Laboratory tests were performed on samples of soil–rock mixture derived from four origins. The tests involved usual index tests to characterize the S–RM, Point Load Strength Index tests on rock particles to get rock strength and direct shear tests (small (i.e. conventional) and large size) on S–RM samples having different gradations. The results are analysed and existing approaches are evaluated for their applicability to S–RM. The analysis indicates that in general the existing approaches do not adequately capture the shear strength response of S–RM. Conceptually Barton–Kjaernsli approach which was originally suggested for rockfill materials was found to the most promising approach. Modifications have been suggested to Barton–Kjaernsli approach to make it applicable to S–RM. [ABSTRACT FROM AUTHOR]

The roughness coefficients of erosion-control coatings based on geomats are studied. The roughness coefficients were calculated following an analysis of relevant publications. The process of determining the roughness coefficient of geomats filled with crushed stone and bitumen-polymer binder is described. Existing formulas for calculating the roughness coefficient of geomats are analyzed to identify the most optimal approaches. The obtained roughness coefficient values are compared with those reported in other studies on similar coatings. [ABSTRACT FROM AUTHOR]

Over the past few years, the concentration of fine particulate matter (PM2.5) in Delhi's atmosphere has progressively increased, resulting in smog episodes and affecting people's health. Therefore, accurate and reliable forecasting of PM2.5 concentration is essential to guide effective precautions before and during extreme pollution events. In this work, soft computing techniques, including Artificial Neural Network and Gaussian Process Regression are employed to predict PM2.5 concentrations in Delhi. Four models, namely, multi-layer feed-forward neural network (MLFFNN), General regression neural network, Gaussian process regression with ARD squared exponential kernel (GPARD_sqexp) and Gaussian process regression with ARD rational quadratic kernel (GPARD_rat_quad) are built using meteorological and air quality data corresponding to a two-year period (2015–2016). The results of the study suggested that MLFFNN showed the best prediction performance among the four models, with testing correlation coefficient (R) 0.949, Root mean square error 30.193, Nash–Sutcliffe efficiency index 0.892 and Mean absolute error 18.388. Moreover, sensitivity analysis performed to understand the importance of different input variables reported that PM10, wind speed, air quality index and aerodynamic roughness coefficient (Z0) are the most critical parameters influencing MLFFNN model forecasts. On the whole, the work has demonstrated that the artificial neural network model is more capable of dealing with PM2.5 forecasting in Delhi urban area than the Gaussian process regression model. [ABSTRACT FROM AUTHOR]

The present study intends to use machine learning (ML) and deep learning (DL) models to forecast PM2.5 concentration at a location in Delhi. For this purpose, multi-layer feed-forward neural network (MLFFNN), support vector machine (SVM), random forest (RF) and long short-term memory networks (LSTM) have been applied. The air pollutants, e.g., CO, Ozone, PM10, NO, NO2, NOx, NH3, SO2, benzene, toluene, as well as meteorological parameters (temperature, wind speed, wind direction, rainfall, evaporation, humidity, pressure, etc.), have been used as inputs in the present study. Moreover, this is one of the first papers that employ aerodynamic roughness coefficient as an input parameter for the prediction of PM2.5 concentration. The result of the study shows that the LSTM model with index of agreement (IA) 0.986, root mean square error (RMSE) 21.510, Nash–Sutcliffe efficiency index (NSE) 0.945, (coefficient of determination)R2 0.945, and (correlation coefficient)R 0.972 is the best performing technique for the prediction of PM2.5 followed by MLFFNN, SVM, and RF models. The sensitivity analysis for the LSTM model reported that PM10, wind speed, NH3, and benzene are the key influencing parameters for the estimation of PM2.5. The findings in this work suggest that the LSTM could advance in PM2.5 forecasting and thus would be useful for developing fine-scale, state-of-the-art air pollution forecasting models. [ABSTRACT FROM AUTHOR]

A timely and cost-effective method of creating inundation maps could assist first responders in allocating resources and personnel in the event of a flood or in preparation of a future disaster. The Height Above Nearest Drainage (HAND) model could be implemented into an on-the-fly flood mapping application for a Canada-wide service. The HAND model requires water level (m) data inputs while many sources of hydrological data in Canada only provide discharge (m3/sec) data. Synthetic rating curves (SRCs), created using river geometry/characteristics and the Manning's formula, could be utilized to provide an approximate water level given a discharge input. A challenge with creating SRCs includes representing how multiple different land covers will slow impact flow due to texture and bulky features (i.e., smooth asphalt versus rocky river channel); this relates to the roughness coefficient (n). In our study, two methods of representing multiple n values were experimented with (a weighted method and a minimum-median method) and were compared to using a fixed n method. A custom ArcGIS tool, Canadian Estimator of Ratings Curves using HAND and Discharge (CERC-HAND-D), was developed to create SRCs using all three methods. Control data were sourced from gauge stations across Canada in the form of rating curves. Results indicate that in areas with medium to medium–high river gradients (S > 0.002 m/m) or with river reaches under 5 km, the CERC-HAND-D tool creates more accurate SRCs (NRMSE = 3.7–8.8%, Percent Bias = −7.8%—9.4%), with the minimum-median method being the preferred n method. [ABSTRACT FROM AUTHOR]

The samples of slightly weathered granite drilled before the construction of Runyang suspended bridge's north anchorage are used as the research object in this paper. Through a lot of shear strength tests of concrete-bedrock rough cement face, the influence of the cement face roughness on the shear strength, shear deformation characteristics and shear failure mechanism were systematically studied. The results show that: The post-peak stress–strain curves of smooth and rough cement faces are obviously different. The shear stress–strain curve of cement face can be generalized into 3 types. The shear stress–strain model of rough cement face is the most general form, and the smooth cement face and no cementation are its special cases. The empirical relation between cement face roughness JRC and shear strength parameters is established by the fitting of experimental data. When the cement face is climbing, the internal friction angle increases with the climbing angle. When the climbing angle is constant, the increase of cohesion is positively correlated with the ratio of fluctuation difference and wavelength. When the climbing thrust is equal to the gnawing thrust, the specimen is transformed from climbing to gnawing. [ABSTRACT FROM AUTHOR]

In the A irrigation district in Yamagata Prefecture, Japan, a rehabilitation project is being implemented due to the aging of the irrigation facilities; 40 years have elapsed since the previous project was conducted. An overflow due to a rise in the canal water level occurred in early May of 2016. An increase in the roughness coefficient caused by algal growth in the canal was thought to be one of the reasons for this overflow. Therefore, the relationship between algal growth and the roughness coefficient in an 11-km section of the canal was analyzed in this study. The objectives of this study were to: (1) clarify the changes in the roughness coefficient associated with the growth of algae; (2) identify the dominant algae in the canal and examine the relationships between the growth/decline of algae and water temperature and sunshine; (3) determine the effectiveness of fiberglass reinforced plastic mortar (FRPM) panels constructed in an attempt to suppress algal growth; and (4) clarify why overflow occurred in 2016 but did not occur again in 2017. Ten algae observation points and four flow velocity measurement points were selected in the target section of the canal. Algae observations and flow velocity measurements were carried out once a week in the irrigation season in 2017. This study found that: (1) the roughness coefficient varied from 0.011 to 0.019 and increased with the growth of algae; (2) the major algae identified in the canal were Ulothrix zonata, Hydrurus foetidus, and microscopic diatoms, and since all of these are cold-water algae they started declining when the water temperature was over 13 °C; (3) the FRPM panels suppressed the growth of U. zonata and suppressed increases in the roughness coefficient; and (4) the main reason for the overflow from the canal in 2016 was that the period suitable for algal growth was longer in 2016 than that in 2017 due to irrigation beginning 10 days earlier in 2016 than in 2017. [ABSTRACT FROM AUTHOR]

Roughness coefficient, also called Manning's coefficient, is one of the most important hydraulic parameters in the rivers. This coefficient, in addition to the flow conditions, depends on streambed characteristics such as type and density of vegetation. In this study, a physical model in a flume with 7 m length, 0.25 m width and 0.25 m height was conducted to evaluate the streambed roughness coefficient and consequently the discharge passing from waterways. Flume bed was filled using uniform sediment with median grain diameter of 1.9 mm, variation coefficient of 1.4 mm and a thickness of 0.4 m. Roughness coefficient variation in the slopes of 0.2, 0.4 ,and 0.6%, discharges of 4, 6, and 8 L/s and vegetation cover densities as 0, 12, 25, and 50% were investigated. To simulate the covering of streambed, vegetation scrub was used in the experiments. The results showed that by increasing the density of vegetation, roughness coefficient increases while with increasing flow velocity, slope and Froude number decreases. By analyzing the data from this study, streambed roughness coefficient was obtained in terms of different variables as applicable relationships for different conditions of flow and streambed. The results of this study with quantifying the effects of various parameters on the roughness coefficient can be used by water engineers. [ABSTRACT FROM AUTHOR]

In this study, cuckoo search is introduced for performing the parameter estimation of shallow wave equation for the first time. Cuckoo search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. These meta-heuristics have been successfully used for solving some optimization problems with promising results. However, this emerging optimization method has not been applied in parameter inversion problem. This study reports a CS-based parameter estimation method to inverse the roughness coefficient and the coefficient of eddy viscosity under some specific conditions. Simulation results and experimental data show that cuckoo search offers a reliable performance for these parameter estimation problems. [ABSTRACT FROM AUTHOR]

Vegetation growing in the water along watercourses has been the subject of several studies since it was recognized that it could have a significant impact on the water flow. It may increase resistance to flow and cause higher water levels. Also, it has an effect on the velocity profiles. Previous investigations on the flow of water through emergent vegetation have shown different results. The purpose of this paper is to investigate, and determine how aquatic vegetation influences flow resistance, water depth and discharge in the Chotárny channel at the Zitný Ostrov area. This area is part of the Danube Lowland (south-west of Slovakia). The channel network at the Žitný Ostrov region was built up for drainage and also to provide irrigation water. The Chotárny channel is one of three main channels of this network. Measurements performed during six years at this channel were used for an evaluation of vegetation impact on flow conditions. The roughness coefficient was used as one way of quantifying this impact. The results show variation of this parameter during the growing season. Vegetation causes resistance to flow; it reduces flow velocities, discharge and increases water depth. [ABSTRACT FROM AUTHOR]

The paper puts forwards some results of studies on hydraulic resistance in a corrugated metal pipe with helical corrugation and smooth invert trough. Recommendations are given for calculating the roughness coefficient for pressure or free-surface water flows. Results of hydraulic studies suggest that pipe filling and slope impacts hydraulic resistance in free-surface flows. [ABSTRACT FROM AUTHOR]

Results are presented of experimental hydraulic studies of a model of a helically corrugated metal pipe culvert. Hydraulic resistance values are considered for water moving under free-flow and surcharged conditions. The influence of pipe slope and pipe filling on the roughness coefficient under free-flow conditions was determined. Recommendations are given for designating a roughness coefficient for the studied helical corrugation under free-flow and surcharged conditions. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to extract land topographical information by analyzing the 3D ground-penetrating radar (GPR) data by the 2D directional continuous wavelet transform method. The first objective is to identify direction of obstacles, and the second is to characterize each one with a special roughness coefficient. Application on real data shows that the proposed tool can enhance the GPR data interpretation. [ABSTRACT FROM AUTHOR]

For the improvement of the conventional stage-discharge curve equation based on just stage, this study proposed a method to estimate river discharge using hydraulic characteristics, such as hydraulic radius, bed slope, depth, etc. Hydraulic gradients in Manning's Equation and Chezy's Equation, both of which are among mean velocity equations, were computed using those characteristics. For that computation, hydrological data actually measured in a laboratory channel and a natural open channel from the Albert University Report were used. And then, river discharge was calculated using the hydraulic gradients. For comparison with the conventional method of inversely calculating roughness coefficients, their power function was formulated. And river discharge estimated using the proposed method was compared with the corresponding one estimated using stage-discharge relations. As a result, the proposed method showed results more approximate to measured discharge. An analysis of discrepancy ratio distribution revealed that non-dimensional errors lied within the ranges of −0.2 to 0.23. This indicates that river discharge estimated using the proposed method showed some improvements in accuracy, compared with river discharge estimated using the conventional stage-discharge curve equation or inverse roughness coefficients. [ABSTRACT FROM AUTHOR]