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Abstract: Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for prediction and forecasting in water resources and environmental engineering. However, despite this high level of research activity, methods for developing ANN models are not yet well established. In this paper, the steps in the development of ANN models are outlined and taxonomies of approaches are introduced for each of these steps. In order to obtain a snapshot of current practice, ANN development methods are assessed based on these taxonomies for 210 journal papers that were published from 1999 to 2007 and focus on the prediction of water resource variables in river systems. The results obtained indicate that the vast majority of studies focus on flow prediction, with very few applications to water quality. Methods used for determining model inputs, appropriate data subsets and the best model structure are generally obtained in an ad-hoc fashion and require further attention. Although multilayer perceptrons are still the most popular model architecture, other model architectures are also used extensively. In relation to model calibration, gradient based methods are used almost exclusively. In conclusion, despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues. Consequently, there is still a need for the development of robust ANN model development approaches. [Copyright &y& Elsevier]

Abstract: The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting. [Copyright &y& Elsevier]

Abstract: This paper compares various training methods available for training multi-layer perceptron (MLP) type of artificial neural networks (ANNs) for modelling the rainfall–runoff process. The training methods investigated include the popular back-propagation algorithm (BPA), real-coded genetic algorithm (RGA), and a self-organizing map (SOM). A SOM was used to first classify the input–output space into different categories and then develop feed-forward MLP models for each category using BPA. The daily average rainfall and streamflow data derived from an existing catchment were employed to develop all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the approach of first classifying the input–output space into different categories using SOM and then developing separate ANN models for different classes trained using BPA performs better than the approach of developing a single ANN rainfall–runoff model trained using BPA. The ANN rainfall–runoff model trained using RGA was able to provide a better generalization of the complex, dynamic, non-linear, and fragmented rainfall–runoff process in comparison with the other approaches investigated in this study. It has been found that the RGA trained ANN model significantly outperformed the ANN model trained using BPA, and was also able to overcome certain limitations of the ANN rainfall–runoff model trained using BPA reported by many researchers in the past. It is noted that the performances of various ANN models should to be evaluated using a wide variety of statistical performance indices rather than relying on a few global error statistics normally employed that are similar in nature to the global error minimized at the output layer of an ANN. [Copyright &y& Elsevier]

Abstract: Infiltration is a key component in the rainfall runoff models employed for runoff prediction. Conventionally, the hydrologists have relied on classical optimization techniques for obtaining the parameters of various infiltration equations. Recently, artificial neural networks (ANNs) have been proposed as efficient tools for modelling and forecasting. This paper proposes the use of ANNs for calibrating infiltration equations. The ANN consists of rainfall and runoff as the inputs and the infiltration parameters as the outputs. Classical optimization techniques were also employed to determine flow hydrographs for comparison purposes. The performances of both the approaches were evaluated using a variety of standard statistical measures in terms of their ability to predict runoff. The results obtained in this study indicate that the ANN technique can be successfully employed for the purpose of calibration of infiltration equations. The regenerated and predicted storms indicate that the ANN models performed better than the classical techniques. It has been found that the ANNs are capable of performing very well in situations of limited data availability since the differences in the performances of the ANNs trained on partial information and the ANNs trained on the complete information was only marginal and the ANN trained on partial information consisted of a more compact architecture. A wide variety of standard statistical performance evaluation measures are needed to properly evaluate the performances of various ANN models rather than relying on a few global error statistics (such as RMSE and correlation coefficient) normally employed. [Copyright &y& Elsevier]