Sediment Runoff Modelling Using ANNs in an Eastern Himalayan Basin, India
01 Jan 2017-pp 73-82
TL;DR: In this paper, the authors have focused on the application of artificial neural network (ANN) technique for sediment-discharge modelling of a headwater river, and compared the two techniques.
Abstract: The amount of sediments transported by headwater rivers plays a crucial role in planning of water resources. Most widely used methods of estimating the sediment in rivers are the empirical methods, and in India, the rating curve technique is most popular. The present study is focused on the application of artificial neural network (ANN) technique for sediment-discharge modelling of a headwater river. For ANN development, daily discharge and suspended sediment concentration data of Subansiri River (an eastern Himalayan river) in India have been used. Rating curves have also been developed with similar data, and comparison of the two techniques has been carried out. It has been observed that the estimates of suspended sediment concentration obtained by ANNs compared to the rating curve technique were much closer to the observed values.
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TL;DR: A multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm to increase the efficiency of the conventional model of the sediment rating curve (SRC).
13 citations
TL;DR: Using evolutionary algorithms in calibrating SRC models prevents data log-transformation and use of correction factors along with increasing in the accuracy of molding results, showed that evolutionary algorithms are appropriate methods for optimizing coefficients of SRC model and their results are much more favorable than those of the conventional SRC Models.
Abstract: Sediment rating curve (SRC) is a conventional and a common regression model in estimating suspended sediment load (SSL) of flow discharge. However, in most cases the data log-transformation in SRC models causing a bias which underestimates SSL prediction. In this study, using the daily stream flow and suspended sediment load data from Shalman hydrometric station on Shalmanroud River, Guilan Province, Iran, SRC equation was derived, and then, using evolutionary algorithms (genetic algorithm and particle swarm optimization algorithm) it was calibrated again. Worth mentioning, before model calibration, to increase the generalization power of the models, using self-organizing map (an unsupervised artificial neural network for data clustering), the data were clustered and then by data sampling, they were classified into two homogeneous groups (calibration and test data set). The results showed that evolutionary algorithms are appropriate methods for optimizing coefficients of SRC model and their results are much more favorable than those of the conventional SRC models or SRC models corrected by correction factors. So that, the sediment rating curve models calibrated with evolutionary algorithms, by reducing the RMSE of the test data set of 5754.02 ton day-1 (in the initial SRC model) to 1681.21 ton day-1 (in the calibrated models by evolutionary algorithms) increased the accuracy of suspended sediment load estimation at a rate of 4072.81 ton day-1. In total, using evolutionary algorithms in calibrating SRC models prevents data log-transformation and use of correction factors along with increasing in the accuracy of molding results.
9 citations
Cites background from "Sediment Runoff Modelling Using ANN..."
...…of erosion as well as changes in the river bed and river bank, the quality of water, along with optimum design and favorable performance of water resource structures (Tayfur 2012; Nourani et al. 2016; Buyukyildiz & Kumcu, 2017; Vercruysse et al. 2017; Sarkar et al. 2017; Salehpour Jam et al. 2017)....
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...INTRODUCTION It is necessary to have adequate up-to-date information about the suspended sediment load (SSL) of rivers and monitor them continually in order to be aware of the watershed sediment yield condition, the amount of erosion as well as changes in the river bed and river bank, the quality of water, along with optimum design and favorable performance of water resource structures (Tayfur 2012; Nourani et al. 2016; Buyukyildiz & Kumcu, 2017; Vercruysse et al. 2017; Sarkar et al. 2017; Salehpour Jam et al. 2017)....
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TL;DR: The findings revealed that the SVR model using hydrological data along with the C-factor can be a cost-effective and promising tool in SSC prediction at the watershed scale.
Abstract: The accurate forecasts and estimations of the amount of sediment transported by rivers are critical concerns in water resource management and soil and water conservation. The identification of appropriate and applicable models or improvements in existing approaches is needed to accurately estimate the suspended sediment concentration (SSC). In recent decades, the utilization of intelligent models has substantially improved SSC estimation. The identification of beneficial and proper input parameters can greatly improve the performance of these smart models. In this regard, we assessed the C-factor of the revised universal soil loss equation (RUSLE) as a new input along with hydrological variables for modeling SSC. Four data-driven models (feed-forward neural network (FFNN); support vector regression (SVR); adaptive neuro-fuzzy inference system (ANFIS); and radial basis function (RBF)) were applied in the Boostan Dam Watershed, Iran. The cross-correlation function (CCF) and partial autocorrelation function (PAFC) approaches were applied to determine the effective lag times of the flow rate and suspended sediment, respectively. Additionally, several input scenarios were constructed, and finally, the best input combination and model were identified through trial and error and standard statistics (coefficient of determination (R2); root mean square error (RMSE); mean absolute error (MAE); and Nash–Sutcliffe efficiency coefficient (NS)). Our findings revealed that using the C-factor can considerably improve model efficiency. The best input scenario in which the C-factor was combined with hydrological data improved the NS by 16.4%, 21.4%, 0.17.5%, and 23.2% for SVR, ANFIS, FFNN, and RBF models, respectively, compared with the models using only hydrological inputs. Additionally, a comparison among the different models showed that the SVR model had about 4.1%, 13.7%, and 23.3% (based on the NS metric) higher accuracy than ANFIS, FFNN, and RBF for SSC estimation, respectively. Thus, the SVR model using hydrological data along with the C-factor can be a cost-effective and promising tool in SSC prediction at the watershed scale.
1 citations
References
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TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Abstract: We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.
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TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
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TL;DR: In particular, tectonically driven increases in chemical weathering may have resulted in a decrease of atmospheric C02 concentration over the past 40 Myr as discussed by the authors. But this was not shown to be the case for the uplift of the Tibetan plateau and positive feedbacks initiated by this event.
Abstract: Global cooling in the Cenozoic, which led to the growth of large continental ice sheets in both hemispheres, may have been caused by the uplift of the Tibetan plateau and the positive feedbacks initiated by this event. In particular, tectonically driven increases in chemical weathering may have resulted in a decrease of atmospheric C02 concentration over the past 40 Myr.
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TL;DR: In this paper, the authors presented a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three-layer feed forward ANN models and demonstrated the potential of such models for simulating the nonlinear hydrologic behavior of watersheds.
Abstract: An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been found useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using physical equations. This study presents a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three-layer feed forward ANN models and demonstrates the potential of such models for simulating the nonlinear hydrologic behavior of watersheds. The nonlinear ANN model approach is shown to provide a better representation of the rainfall-runoff relationship of the medium-size Leaf River basin near Collins, Mississippi, than the linear ARMAX (autoregressive moving average with exogenous inputs) time series approach or the conceptual SAC-SMA (Sacramento soil moisture accounting) model. Because the ANN approach presented here does not provide models that have physically realistic components and parameters, it is by no means a substitute for conceptual watershed modeling. However, the ANN approach does provide a viable and effective alternative to the ARMAX time series approach for developing input-output simulation and forecasting models in situations that do not require modeling of the internal structure of the watershed.
1,382 citations