scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Multivariate modelling of water resources time series using artificial neural networks

01 Apr 1995-Hydrological Sciences Journal-journal Des Sciences Hydrologiques (Taylor & Francis Group)-Vol. 40, Iss: 2, pp 145-163
TL;DR: The artificial neural network approach for the synthesis of reservoir inflow series differs from the traditional approaches in synthetic hydrology in the sense that it focuses on the role of reinforcement learning in the decision-making process.
Abstract: The artificial neural network (ANN) approach described in this paper for the synthesis of reservoir inflow series differs from the traditional approaches in synthetic hydrology in the sense that it belongs to a class of data-driven approaches as opposed to traditional model driven approaches. Most of the time series modelling procedures fall within the framework of multivariate autoregressive moving average (ARMA) models. Formal statistical modelling procedures suggest a fourstage iterative process, namely, model selection, model order identification, parameter estimation and diagnostic checks. Although a number of statistical tools are already available to follow such a modelling process, it is not an easy task, especially if higher order vector ARMA models are used. This paper investigates the use of artificial neural networks in the field of synthetic inflow generation. The various steps involved in the development of a neural network and a ultivariate autoregressive model for synthesis are pr...
Citations
More filters
Journal ArticleDOI
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Abstract: Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.

2,181 citations


Cites background or methods from "Multivariate modelling of water res..."

  • ..., 1994) or by developing separate models for different months of the year (Raman and Sunilkumar, 1995)....

    [...]

  • ...In only two cases was an independent test set used in addition to the training and validation sets (Golob et al., 1998; Raman and Sunilkumar, 1995)....

    [...]

  • ...In other papers, the number of lags was chosen arbitrarily (e.g. Karunanithi et al., 1994; Raman and Sunilkumar, 1995; Recknagel, 1997)....

    [...]

Journal ArticleDOI
TL;DR: The role of ANNs in various branches of hydrology has been examined here and it is suggested that ANNs should be considered as a “bridge network” to other types of neural networks.
Abstract: This paper forms the second part of the series on application of artificial neural networks (ANNs) in hydrology. The role of ANNs in various branches of hydrology has been examined here. It is foun...

1,106 citations

Journal ArticleDOI
TL;DR: A template is proposed in order to assist the construction of future ANN rainfall-runoff models and it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
Abstract: This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, character- ized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conven- tional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological 'rules' from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.

813 citations

Journal ArticleDOI
TL;DR: The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models.
Abstract: This paper provides a discussion of the development and application of Artificial Neural Networks (ANNs) to flow forecasting in two flood-prone UK catchments using real hydrometric data. Given relatively brief calibration data sets it was possible to construct robust models of 15-min flows with six hour lead times for the Rivers Amber and Mole. Comparisons were made between the performance of the ANN and those of conventional flood forecasting systems. The results obtained for validation forecasts were of comparable quality to those obtained from operational systems for the River Amber. The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models. However, further research is required to determine the optimum ANN training period for a given catchment, season and hydrological contexts.

610 citations

Journal ArticleDOI
TL;DR: Results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc.

568 citations


Cites background from "Multivariate modelling of water res..."

  • ...However, Raman and Sunilkumar (1995) reported that good results could be obtained by using real world observations directly (i.e. without normalizing and standardizing the data prior to input)....

    [...]

References
More filters
Book
01 Jan 1970
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Abstract: From the Publisher: This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.

19,748 citations

Journal ArticleDOI
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
Abstract: time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time series analysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series analysis forecasting and control pambudi, time series analysis forecasting and control george e, time series analysis forecasting and control ebook, time series analysis forecasting and control 5th edition, time series analysis forecasting and control fourth, time series analysis forecasting and control amazon, wiley time series analysis forecasting and control 5th, time series analysis forecasting and control edition 5, time series analysis forecasting and control 5th edition, time series analysis forecasting and control abebooks, time series analysis for business forecasting, time series analysis forecasting and control wiley, time series analysis forecasting and control book 1976, time series analysis forecasting and control researchgate, time series analysis forecasting and control edition 4, time series analysis forecasting amp control forecasting, george box publications department of statistics, time series analysis forecasting and control london, time series analysis forecasting and control an, time series analysis forecasting and control amazon it, box g e p and jenkins g m 1976 time series, time series analysis forecasting and control pdf slideshare, time series analysis forecasting and control researchgate, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, time series wikipedia, time series analysis forecasting and control abebooks, time series analysis forecasting and control, forecasting and time series analysis using the sca system, time series analysis forecasting and control by george e, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, box and jenkins time series analysis forecasting and control, time series analysis forecasting and control ebook, time series analysis forecasting and control, time series analysis and forecasting cengage, 6 7 references itl nist gov, time series analysis forecasting and control george e, time series analysis and forecasting statgraphics, time series analysis forecasting and control fourth edition, time series analysis forecasting and control, time series analysis forecasting and control wiley, time series analysis forecasting and control in

10,118 citations


"Multivariate modelling of water res..." refers methods in this paper

  • ...These include autoregressive moving average (ARMA) models (Box & Jenkins, 1970), disaggregation models (Valencia & Schaake, 1973), models based on the concept of pattern recognition (Panu & Unny, 1980)....

    [...]

  • ...This strategy, formalized by Box & Jenkins (1970) and advocated by Salas et al. (1980), is an iterative procedure of model building to ensure satisfactory model fitting and utilization....

    [...]

Journal ArticleDOI
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Abstract: Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets.

7,798 citations

Journal ArticleDOI
01 May 1971

7,355 citations