Assessing effects of model complexity and structure on predictions of hydrological responses using serial and parallel model design

By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub-model with a water quality sub-model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25-year high-resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial-based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub-model with one set of scale assumptions onto the front end of another sub-model with a different set of scale assumptions.     

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008-2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.     

Suspended sediment yield modeling in Mahanadi River,India by multi-objective optimization hybridizing artificial intelligence algorithms

River sediment produced through weathering is one of the principal landscape modification processes on earth.Rivers are an integral part of the hydrologic cycle and are the major geologic agents that erode the continents and transport water and sediments to the oceans.Estimation of suspended sediment yield is always a key parameter for planning and management of any river system.It is always challenging to model sediment yield using traditional mathematical models because they are incapable of handling the complex non-linearity and non-stationarity.The suspended sediment modeling of the river depends on the number of factors such as rock type,relief,rainfall,temperature,water discharge and catchment area.In this study,we proposed a hybrid genetic algorithm-based multi-objective optimization with artificial neural network(GA-MOO-ANN)with automated parameter tuning model using these factors to estimate the suspended sediment yield in the entire Mahanadi River basin.The model was validated by comparing statistically with other models,and it appeared that the GA-MOO-ANN model has the lowest root mean squared error(0.009)and highest coefficient of correlation(0.885)values among all comparative models(traditional neural network,multiple linear regression,and sediment rating curve)for all stations.It was also observed that the proposed model is the least biased(0.001)model.Thus,the proposed GA-MOOANN is the most capable model,compared to other studied models,for estimating the suspended sediment yield in the entire Mahanadi river basin,India.The results also suggested that the proposed GA-MOO-ANN model is unable to estimate suspended sediment yield satisfactorily at gauge stations having very small catchment areas whereas performing satisfactorily on locations having moderate to the large catchment area.The models provide the best result at Tikarapara,the gauge station location in the extreme downstream,having the largest catchment area.     

Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.     

Artificial neural networks for sheet sediment transport

Abstract

Sheet sediment transport was modelled by artificial neural networks (ANNs). A three-layer feed-forward artificial neural network structure was constructed and a back-propagation algorithm was used for the training of ANNs. Event-based, runoff-driven experimental sediment data were used for the training and testing of the ANNs. In training, data on slope and rainfall intensity were fed into the network as inputs and data on sediment discharge were used as target outputs. The performance of the ANNs was tested against that of the most commonly used physically-based models, whose transport capacity was based on one of the dominant variables—flow velocity (V), shear stress (SS), stream power (SP), and unit stream power (USP). The comparison results revealed that the ANNs performed as well as the physically-based models for simulating nonsteady-state sediment loads from different slopes. The performances of the ANNs and the physically-based models were also quantitatively investigated to estimate mean sediment discharges from experimental runs. The investigation results indicated that better estimations were obtained for V over mild and steep slopes, under low rainfall intensity; for USP over mild and steep slopes, under high rainfall intensity; for SP and SS over very steep slopes, under high rainfall intensity; and for ANNs over steep and very steep slopes, under very high rainfall intensities.     

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash–Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.     

Predicting characteristics of dune bedforms using PSO-LSSVM

Dunes have a large influence on hydraulic roughness, and, thereby, on water levels which could affect the navigability of rivers and performance of hydraulic structures. The present study investigated the variation of geometric and topographic characteristics of dune bedforms and flow features as measured in laboratory studies(data sets from laboratory experiments) to estimate the roughness coefficient and characteristics of dune height. The Least Squares Support Vector Machine(LSSVM), which was optimized using Particle Swarm Optimization(PSO), was used as the Meta model approach to predict the values of interest. Developed models were separated into three categories: modeling using flow characteristics,modeling of flow and bedform characteristics, and modeling by using flow and sediment characteristics.It was found that for estimation of the roughness coefficient in open channels with dune bedforms,models developed based on flow and sediment characteristics performed more successfully. The model with input parameters of flow and grain Reynolds numbers(Re and R_b, respectively) and the ratio of the hydraulic radius(R) to the median grain diameter(D_(50)) yields a squared correlation coefficient(R2) of0.8609, a coefficient of determination(DC) of 0.7361,and a root mean square error(RMSE) of 0.0034 for a test series of Manning's roughness coefficient which was the most accurate model. Results proved the key role of flow Reynolds number(Re) values as an input feature for all models predicting the roughness coefficient. Accordingly, classic approaches led to poor results in comparison. On the other hand, results obtained for estimated values of relative dune height led to moderate prediction quality, which albeit,outperformed classic approaches.     

Magnetotelluric inversion for azimuthally anisotropic resistivities employing artificial neural networks

An extension of an artificial neural network (ANN) approach to solve the magnetotelluric (MT) inverse problem for azimuthally anisotropic resistivities is presented and applied for a real dataset. Three different model classes, containing general 1-D and 2-D azimuthally anisotropic features, have been considered. For each model class, characteristics of three-layer feed forward ANNs trained through an error back propagation algorithm have been adjusted to approximate the inverse modeling function. It appears that, at least for synthetic models, reasonable results would be obtained by applying the amplitudes of the complex impedance tensor elements as inputs. Furthermore, the Levenberg-Marquart algorithm possesses optimal performance as a learning paradigm for this problem. The evaluation of applicability of the trained ANNs for unknown data sets excluded from the learning procedure reveals that the trained ANNs possess acceptable interpolation and extrapolation abilities to estimate model parameters accurately. This method was also successfully used for a field dataset wherein anisotropy had been previously recognized.     

The Utilization and Modeling of Photo-Fenton Process as a Single Unit in Textile Wastewater Treatment

Studies on the direct application of the photo-Fenton process (PFOP) to disinfect and decontaminate textile wastewater are rare. The output of the artificial neural network (ANN) models applied to the wastewater of a textile factory producing woven fabrics, which is used to assess the efficiency of the PFOP process, are investigated and compared with each other in this study. The highest PFOP efficiency is obtained at a pH of 3. Chemical oxygen demand (COD), suspended solids (SS) and color removal rates are 94%, 90%, and 96%, respectively. The data are modeled with ANNs and nonlinear external input autoregressive ANNs (NARX-ANN) using the MATLAB R2020a software program. Both Levenberg–Marquardt (trainlm) and scaled conjugate gradient (trainscg) algorithms are employed in the ANN and NARX-ANN models, whereas hyperbolic tangent sigmoid (Tansig) and logistic sigmoid (Logsig) functions are superimposed on the hidden layer in the ANN model, and Tansig functions are superimposed on the NARX-ANN model. It is determined that the developed ANN models are more effective in estimating the PFOP efficiency. The mean squared error is 0.000 953, and the coefficient of determination (R²) is 0.96 661.     

A hybrid artificial neural network-numerical model for ground water problems

Numerical models constitute the most advanced physical-based methods for modeling complex ground water systems. Spatial and/or temporal variability of aquifer parameters, boundary conditions, and initial conditions (for transient simulations) can be assigned across the numerical model domain. While this constitutes a powerful modeling advantage, it also presents the formidable challenge of overcoming parameter uncertainty, which, to date, has not been satisfactorily resolved, inevitably producing model prediction errors. In previous research, artificial neural networks (ANNs), developed with more accessible field data, have achieved excellent predictive accuracy over discrete stress periods at site-specific field locations in complex ground water systems. In an effort to combine the relative advantages of numerical models and ANNs, a new modeling paradigm is presented. The ANN models generate accurate predictions for a limited number of field locations. Appending them to a numerical model produces an overdetermined system of equations, which can be solved using a variety of mathematical techniques, potentially yielding more accurate numerical predictions. Mathematical theory and a simple two-dimensional example are presented to overview relevant mathematical and modeling issues. Two of the three methods for solving the overdetermined system achieved an overall improvement in numerical model accuracy for various levels of synthetic ANN errors using relatively few constrained head values (i.e., cells), which, while demonstrating promise, requires further research. This hybrid approach is not limited to ANN technology; it can be used with other approaches for improving numerical model predictions, such as regression or support vector machines (SVMs).     

Neural networks to simulate regional ground water levels affected by human activities

In arid regions, human activities like agriculture and industry often require large ground water extractions. Under these circumstances, appropriate ground water management policies are essential for preventing aquifer overdraft, and thereby protecting critical ecologic and economic objectives. Identification of such policies requires accurate simulation capability of the ground water system in response to hydrological, meteorological, and human factors. In this research, artificial neural networks (ANNs) were developed and applied to investigate the effects of these factors on ground water levels in the Minqin oasis, located in the lower reach of Shiyang River Basin, in Northwest China. Using data spanning 1980 through 1997, two ANNs were developed to model and simulate dynamic ground water levels for the two subregions of Xinhe and Xiqu. The ANN models achieved high predictive accuracy, validating to 0.37 m or less mean absolute error. Sensitivity analyses were conducted with the models demonstrating that agricultural ground water extraction for irrigation is the predominant factor responsible for declining ground water levels exacerbated by a reduction in regional surface water inflows. ANN simulations indicate that it is necessary to reduce the size of the irrigation area to mitigate ground water level declines in the oasis. Unlike previous research, this study demonstrates that ANN modeling can capture important temporally and spatially distributed human factors like agricultural practices and water extraction patterns on a regional basin (or subbasin) scale, providing both high-accuracy prediction capability and enhanced understanding of the critical factors influencing regional ground water conditions.     

Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes

Mitigation of sediment deposition in lined open channels is an essential issue in hydraulic engineering practice.Hence,the limiting velocity should be determined to keep the channel bottom clean from sediment deposits.Recently,sediment transport modeling using various artificial intelligence(AI) techniques has attracted the interest of many researchers.The current integrated study highlights unique insight for modeling of sediment transport in sewer and urban drainage systems.A novel methodology based on the combination of sensitivity and uncertainty analyses with a machine learning technique is proposed as a tool for selection of the best input combination for modeling process at non-deposition conditions of sediment transport.Utilizing one to seven dimensionless parameters,127 models are developed in the current study.In order to evaluate the different parameter co mbinations and select the training and te sting data,four strategies are considered.Considering the densimetric Froude number(Fr) as the dependent parameter,a model with independent parameters of volumetric sediment concentration(C_V) and relative particle size(d/R) gave the best results with a mean absolute relative error(MARE) of 0.1 and a root means square error(RMSE) of 0.67.Uncertainty analysis is applied with a machine learning technique to investigate the credibility of the proposed methods.The percentage of the observed sample data bracketed by95% predicted uncertainty bound(95 PPU) is computed to assess the uncertainty of the best models.     

Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin

A total dissolved solid (TDS) is an important indicator for water quality assessment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationship of mineral salt composition with TDS. In the present study, four artificial intelligence approaches, namely artificial neural networks (ANNs), two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast TDS in river water over a period of 18 years at seven different sites. Five different GEP, ANFIS and ANN models comprising various combinations of water quality and flow variables from Zarinehroud basin in northwest of Iran were developed to forecast TDS variations. The correlation coefficient (R), root mean square error and mean absolute error statistics were used for evaluating the accuracy of models. Based on the comparisons, it was found that the GEP, ANFIS-GP, ANFIS-SC and ANN models could be employed successfully in forecasting TDS variations. A comparison was made between these artificial intelligence approaches which emphasized the superiority of GEP over the other intelligent models.     

Coupled modeling of rainfall-induced floods and sediment transport at the catchment scale

Rainfall-induced floods may trigger intense sediment transport on erodible catchments, especially on the Loess Plateau in China, which in turn modifies the floods. However, the role of sediment transport in modifying floods has to date remained poorly understood. Concurrently, traditional hydrodynamic models for rainfall-induced floods typically ignore sediment transport, which may lead to inaccurate results for highly erodible catchments. Here, a two-dimensional (2D) coupled shallow water hydro-sediment-morphodynamic (SHSM) model, based on the Finite Volume Method on unstructured meshes and parallel computing, is proposed and applied to simulate rainfall-induced floods in the Zhidan catchment on the Loess Plateau, Shaanxi Province, China. For six historical floods of return periods up to 2 years, the numerical results compare well with observations of discharge hydrographs at the catchment outlet. The computed runoff-sediment yield relation is quantitatively reasonable as compared with other catchments under similar geographical conditions. It is revealed that neglecting sediment transport leads to underestimation of peak discharge of the flood by 14%–45%, whilst its effect on the timing of the peak discharge varies for different flood events. For 18 design floods with return periods of 10–500 years, sediment transport may lead to higher peak discharge by around 9%–15%. The temporal pattern of concentrated rainfall in a short period may lead to a larger exponent value of the power function for the runoff-sediment yield relation. The current finding leads us to propose that incorporating sediment transport in rainfall-induced flood modeling is warranted. The SHSM model is applicable to flood and sediment modeling at the catchment scale in support of risk management and water and soil management.     

Modelling transport dynamics of contaminated sediments in the headwater of a hydropower plant at the Upper Rhine River

Sediments are an essential habitat compartment in rivers, which is a subject to dynamic transport processes. In many rivers, the fine deposited sediments are contaminated with heavy metals and organic compounds. Contaminated deposits are considered as potential hot spots because of the risk of the mobilization under erosive hydraulic conditions. Numerical models for particulate contaminant transport are then necessary and can be applied to estimate and predict the potential impact of mobilized contaminants as an important contribution to sediment management. This paper focuses on the quantification of the amount of contaminated sediments resuspended during the extreme flood event in 1999 and the prediction of deposition one year after the flood event. To assess such erosive flood event, a 2D numerical transport model was developed to analyse the dynamics of erosion and sedimentation processes in the headwater of a cross dam at the Upper Rhine River. The dam consists of a weir, a hydropower plant, and a navigation lock. As the weir is operating only for flood management, a huge amount of sediment highly contaminated with the hexachlorobenzene (HCB) was deposited in the weir zone. Therefore, numerical simulations were performed to determine the spatial and temporal distribution of deposited contaminated sediments as depending on the river discharge and its distribution to the hydraulic structures. The numerical investigation presented here is taken as a retrospective analysis of the contaminated sediment dynamics in the headwater to improve future sediment management.     

MODELING SEDIMENT PROCESSES ON SMALL WATERSHEDS: A CONCEPTUAL FRAMEWORK Ⅱ. CONCENTRATED FLOW PROCESSES

L INTRODUCTIONThe purpose of this paper is to present a general modeling framework that can serveas a conceptual basis for developing sediment process models by concentrated flow systems on small watersheds. A survey of fundamental principles for developing sedimentprocess models is made with particular emphasis on the effects of space and time averaging on the governing equations. Starting from the most general one--dimensional,unsteady model of sediment processes, simpler model structur…     

Multi-objective and multi-scheme research on water and sediment regulation potential of reservoirs in the upper Yellow River

The problems of the deteriorating relation between water and sediment, and the escalating conflict between water supply and demand in the upper Yellow River(YR), need to be addressed. Reservoirs of Longyangxia, Liujiaxia, and Heishanxia(Long-Liu-Hei) in the upper YR are taken as the research object, and a multi-objective, water-sediment optimal operation model for cascade reservoirs Long-Liu-Hei has been developed. This operation model considers the comprehensive requirements of water supply, po...     

A BRIEF REVIEW OF SEVERAL MODELS OF TOPOGRAPHIC EVOLUTION

With steady development of mathematical-physical models and computer technology, numerous methods of topographic simulation have emerged during the past decades. A major challenge in the modeling is how to accurately and efficiently describe processes of surface erosion at different spatial scales. This review focuses on the physical processes controlling surface erosion, including river erosion and hillslope erosion. Four popular models of topographic simulation (CASCADE, CHILD, FastScape and DAC models)and their applications are presented. Although these models have become more sophisticated in recent years, there are still some issues unsolved regarding the basics of the physical erosion processes. For example, some factors have not been taken into account, such as the impacts of changes in grain size and sediment budget during transportation on river erosion and the measurements of the rock erodibilities for various lithologies. Moreover, there is no topographic index that can be used to evaluate the modeling results. Therefore, it would be helpful to combine the models of topographic simulation with other numerical models, e.g. the low-temperature thermochronometric data modeling, to provide better constraints on the terrain modeling.     

Fully coupled mathematical modeling of turbidity currents over erodible bed

Turbidity currents may feature active sediment transport and rapid bed deformation, such as those responsible for the erosion of many submarine canyons. Yet previous mathematical models are built upon simplified governing equations and involve steady flow and weak sediment transport assumptions, which are not in complete accordance with rigorous conservation laws. It so far remains unknown if these could have considerable impacts on the evolution of turbidity currents. Here a fully coupled modeling study is presented to gain new insights into the evolution of turbidity currents. The recent analysis of the multiple time scales of subaerial sediment-laden flows over erodible bed [Cao Z, Li Y, Yue Z. Multiple time scales of alluvial rivers carrying suspended sediment and their implications for mathematical modeling. Adv Water Resour 2007;30(4):715–29] is extended to subaqueous turbidity currents to complement the fully coupled modeling. Results from numerical simulations show the ability of the present coupled model to reproduce self-accelerating turbidity currents. Comparison among the fully and partially coupled and decoupled models along with the analysis of the relative time scale of bed deformation explicitly demonstrate that fully coupled modeling is essential for refined resolution of those turbidity currents featuring active sediment transport and rapid bed deformation, and existing models based on simplified conservation laws need to be reformulated.