Effective and Efficient Modeling for Streamflow Forecasting

Gupta, Hoshin; Hsü, K.; Sorooshian, Soroosh

doi:10.1007/978-94-015-9341-0_2

Cited by 17 publications

(18 citation statements)

References 24 publications

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“…There are a number of ANN architecture types, the use of which depends on the intended purpose (Tadeusiewicz 1993;Osowski 2013). In streamflow modelling the most commonly used type of network is the feed-forward multilayer perceptron, also known as MLP (Gupta et al 2000). The process of the ANN model creation can be divided into several steps: (1) collection of the representative data that will be needed to establish the network; (2) definition of the network structure and processing features; (3) learning of the network; and (4) results verification.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks in proglacial discharge simulation: application and efficiency analysis in comparison to the multivariate regression; a case study of waldemar river (svalbard)

Nowak

Sobota

2015

Geografiska Annaler: Series A, Physical Geography

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Artificial neural networks were applied to simulate runoff from the glacierized part of the Waldemar River catchment (Svalbard) based on hydrometeorological data collected in the summer seasons of 2010, 2011 and 2012. Continuous discharge monitoring was performed at about 1 km from the glacier snout, in the place where the river leaves the marginal zone. Averaged daily values of discharge and selected meteorological variables in a number of combinations were used to create several models based on the feed‐forward multilayer perceptron architecture. Due to specific conditions of melt water storing and releasing, two groups of models were established: the first is based on meteorological inputs only, while second includes the preceding day's mean discharge. Analysis of the multilayer perceptron simulation performance was done in comparison to the other black‐box model type, a multivariate regression method based on the following efficiency criteria: coefficient of determination (R2) and its adjusted form (adj. R2), weighted coefficient of determination (wR2), Nash–Sutcliffe coefficient of efficiency, mean absolute error, and error analysis. Moreover, the predictors' importance analysis for both multilayer perceptron and multivariate regression models was done. The performed study showed that the nonlinear estimation realized by the multilayer perceptron gives more accurate results than the multivariate regression approach in both groups of models.

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Section: Introductionmentioning

confidence: 99%

Artificial neural networks in proglacial discharge simulation: application and efficiency analysis in comparison to the multivariate regression; a case study of waldemar river (svalbard)

Nowak

Sobota

2015

Geografiska Annaler: Series A, Physical Geography

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“…Time series forecasting has an important role for water resources planning and management. Conventionally, researchers have employed traditional methods such as AR, ARMA, ARIMA, etc, (Gupta & Sorooshian, 2000;Cigizoglu & Kisi, 2005;Cigizoglu, 2008). The daily discharge data, from actual field observed data in Ruta Ranquil (Colorado River), was employed first time to develop several models investigated in this study, explore the performance of the ANN approach for the estimation of river flow, and compare its result to those of the autoregression technique (AR).…”

Section: Discussionmentioning

confidence: 99%

“…The former are specifically designed to mathematically simulate the subprocesses and physical mechanisms that govern the hydrological cycle, usually incorporating simplified forms of physical laws and being generally nonlinear, timeinvariant, and deterministic. These techniques, although they use representative parameters of watershed characteristics (Gupta et al, 2000), they ignore the spatial distribution, the time-varying properties and the stochastic nature of the rainfall. Yang & Michel (2000) state that conceptual watershed models are reliable in forecasting the most important features of the hydrograph; but, the implementation and calibration of such a model can typically present some difficulty, because it requires sophisticated mathematical tools, significant amounts of calibration data and some degree of expertise and experience with the model (Zhang et al, 1998;Sudheer et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Prediction of water flows in Colorado River, Argentina

Pierini

Gómez

Telesca

2012

lajar

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ABSTRACT. The identification of suitable models for predicting daily water flow is important for planning and management of water storage in reservoirs of Argentina. Long-term prediction of water flow is crucial for regulating reservoirs and hydroelectric plants, for assessing environmental protection and sustainable development, for guaranteeing correct operation of public water supply in cities like Catriel, 25 de Mayo, Colorado River and potentially also Bahía Blanca. In this paper, we analyze in Buta Ranquil flow time series upstream reservoir and hydroelectric plant in order to model and predict daily fluctuations. We compare results obtained by using a three-layer artificial neural network (ANN), and an autoregressive (AR) model, using 18 years of data, of which the last 3 years are used for model validation by means of the root mean square error (RMSE), and measure of certainty (Skill). Our results point out to the better performance to predict daily water flow or refill them of the ANN model performance respect to the AR model. Keywords: prediction, time series, neural networks, autoregressive models, flows, Colorado River, Argentina. Predicción de caudales en río Colorado, ArgentinaRESUMEN. La identificación de modelos adecuados para predecir caudales diarios es importante para la planificación y la gestión de almacenamiento de agua en los embalses de la Argentina. La predicción a largo plazo del caudal es crucial para la regulación de los embalses y centrales hidroeléctricas, evaluar la protección del medio ambiente y el desarrollo sostenible, garantizar el correcto funcionamiento del abastecimiento público de agua en ciudades como Catriel, 25 de Mayo, río Colorado y también, eventualmente, en Bahía Blanca. En este trabajo, se analizan series de tiempo de caudales de agua, arriba del embalse y de la planta hidroeléctrica en Buta Ranquil, para modelar y predecir las fluctuaciones diarias. Se comparan los resultados obtenidos mediante el uso de una red neuronal artificial (ANN) de tres capas y un modelo autoregresivo (AR), con 18 años de datos, cuyos últimos 3 años se utilizan para la validación del modelo por medio de la raíz del error cuadrado medio (RMSE) y medida de certeza (Skill). Para predecir o rellenar el caudal diario, los resultados indican que el mejor desempeño es del ANN con respecto al modelo AR.

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“…This NN configuration has been used successfully in many water resources systems applications and shows generally good abilities to model hydrological time series (Chakraborty et al, 1992;Gupta et al, 2000;ASCE-TCAANNH, 2000).…”

Section: Topologymentioning

confidence: 99%

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Ochoa-Rivera¹,

García-Bartual²,

Andreu³

2002

Hydrol. Earth Syst. Sci.

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A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN) deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain), while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2) model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation.

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Effective and Efficient Modeling for Streamflow Forecasting

Cited by 17 publications

References 24 publications

Artificial neural networks in proglacial discharge simulation: application and efficiency analysis in comparison to the multivariate regression; a case study of waldemar river (svalbard)

Artificial neural networks in proglacial discharge simulation: application and efficiency analysis in comparison to the multivariate regression; a case study of waldemar river (svalbard)

Prediction of water flows in Colorado River, Argentina

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

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