The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black-box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall-runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall-runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall-runoff process. The results demonstrate that the hidden neurons in the ANN rainfall-runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. © 2004 John Wiley and Sons, Ltd.

K. P. Sudheer

Department Of Civil Engineering

Computer simulation

infiltration

Neural Networks

Rain

Watersheds

BASE FLOW

Physical processes

Hydrology

Artificial neural network

BASEFLOW

hydrograph

Hydrological modeling

Rainfall-runoff modeling

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Hydrological Processes

This study investigates the potential of nonlinear local function approximation in a Takagi–Sugeno (TS) fuzzy model for river flow forecasting. Generally, in a TS framework, the local approximation is performed by a linear model, while in this approach, linear function approximation is substituted using a nonlinear function approximation. The primary hypothesis herein is that the process being modeled (rainfall–runoff in this study) is highly nonlinear, and a linear approximation at the local domain might still leave a lot of unexplained variance by the model. In this study, subtractive clustering technique is used for domain partition, and neural network is used for function approximation. The modeling approach has been tested on two case studies: Kolar basin in India and Kentucky basin in USA. The results of fuzzy nonlinear local approximation (FNLLA) model are highly promising. The performance of the FNLLA is compared with that of a pure fuzzy inference system (FIS), and it is observed that both the models perform similar at 1-step-ahead forecasts. However, the FNLLA performs much better than FIS at higher lead times. It is also observed that FNLLA forecasts the river flow with lesser error compared to FIS. In the case of Kolar River, more than 40 % of the total data are forecasted with <2 % error by FNLLA at 1 h ahead, while the corresponding value for FIS is only 20 %. In the case of 3-h-ahead forecasts, these values are 25 % for FNLLA and 15 % for FIS. Performance of FNLLA in the case of Kentucky River basin was also better compared to FIS. It is also found that FNLLA simulates the peak flow better than FIS, which is certainly an improvement over the existing models. © 2014, The Natural Computing Applications Forum.

Neural Computing and Applications

River flow forecasting through nonlinear local approximation in a fuzzy model

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. © 2010 Elsevier Ltd. All rights reserved.

Environmental Modelling and Software

Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

Modeling of rainfall runoff (R-R) processes is useful in many water resources management activities. Traditionally, hydrologists have employed deterministic/conceptual methods for R-R modeling. Recently, Artificial Neural Networks (ANNs) have become popular tools for R-R modeling. This paper reviews the literature on and presents state-of-the-art approaches to ANN R-R modeling. Certain aspects of ANN R-R modeling have been covered in greater detail. These include input selection, data division, ANN training, hybrid modeling, and extrapolation beyond the range of training data. There is a strong need to carry out extensive research on these aspects while developing ANN R-R models. © 2009 Taylor & Francis Group, LLC.

ISH Journal of Hydraulic Engineering

Rainfall runoff modelling using neural networks: State-Of-The-Art and future research needs

The paper describes some of the recent developments and applications for knowledge extraction from trained artificial neural network based hydrological models. The methods are in general derived from the casual relationship between inputs and outputs, and analysis of the hidden neuron behaviour. The discussions are focused on works related to explaining the internal behaviour and embedded physical process identification in a trained ANN rainfall-runoff model. It is envisaged that knowledge extraction is important for artificial neural networks to gain wider degree of acceptance especially in the context of hydrologic modeling. Therefore, this domain has to become a major field of research since it validates the use of neural networks for applications where reasons or explanations on why or how a result has been achieved are important. © 2009 Taylor & Francis Group, LLC.

Recent advances in knowledge extraction from neural network based hydrologic models

This paper presents a novel hybrid linear-neural (LN) model formulation to effectively model rainfall-runoff processes. The central idea of the proposed model framework is that the hidden layer of an artificial neural network (ANN) model be designed with a combination of linear and nonlinear neurons. A training algorithm for the proposed model is designed based on minimum description length criteria. The advantage of the algorithm is that the final architecture of the LN model is arrived at during the training process, thus avoiding selection from a class of models. The proposed model has been developed and evaluated for its performance for forecasting the river flow of Kolar basin, in India. The values of three performance evaluation criteria, namely, the coefficient of efficiency, the root-mean-square error, and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 hour in advance by the LN model. The value of the relative error in peak flow prediction was within reasonable limits for the model. The forecasts by the LN model at higher lead times (up to 6 hours) are found to be good. A relative evaluation of LN model performance with that of an ANN model and of a multiple linear regression model indicates that the LN model effectively combines the strength of the other two, implying that the LN model seems to be well suited to exploit the information to model the nonlinear dynamics of the rainfall-runoff process. Copyright 2006 by the American Geophysical Union.

Fulltext

Water Resources Research

A hybrid linear-neural model for river flow forecasting

A data-driven algorithm for constructing artificial neural network rainfall-runoff models

IEEE Transactions on Neural Networks

Interpretation of artificial neural networks by means of fuzzy rules

Progress in Physical Geography

Hydrological modelling using artificial neural networks

Journal of Hydrologic Engineering

Artificial Neural Networks in Hydrology. I: Preliminary Concepts

Identification of physical processes inherent in artificial neural network rainfall runoff models

Journal	Hydrological Processes
ISSN	08856087
Open Access	No