Journal of Hydrology (2006) 329, 274–276

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DISCUSSION

Comment on ‘‘Integration of artificial neuralnetworks with conceptual models in rainfall-runoffmodeling’’ by Jieyun Chen and Barry J. Adams, 2005.J. Hydrol. doi:10.1016/j.jhydrol.2005.06.017

Ashu Jain *,1

Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India

Received 27 September 2005; received in revised form 23 December 2005; accepted 17 February 2006

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The research in the area of rainfall runoff modeling is mov-ing in a direction of decomposition of data and developingdifferent models for different data categories; and integra-tion of available techniques including conceptual and softcomputing methods. In their study, the authors proposed ahybrid rainfall-runoff model that integrates artificial neuralnetworks (ANNs) and conceptual models. The spatial varia-tions in rainfall, and the heterogeneity of water character-istics were modelled by sub-dividing a catchment into threesub-catchments. The flood hydrographs at the three individ-ual sub-catchments were computed using conceptual meth-ods and they were super-imposed non-linearly using ANNsand linearly using regression. Two statistical performancemeasures, MSE and R2, were used to evaluate performance.The discusser would like to highlight some of the issues re-lated to the work presented that either need a clarificationor a response from the authors. The authors’ responsewould greatly benefit the scientific community working onthe ANN hydrologic models and related areas.

022-1694/$ - see front matter �c 2006 Elsevier B.V. All rights reserveoi:10.1016/j.jhydrol.2006.02.034

* Tel.: +91 512 259 7411; fax: +91 512 259 7395.E-mail addresses: ashujain@iitk.ac.in, a.jain@leeds.ac.uk.

1 Present address: School of Geography, University of Leeds,eeds LS2 9JT, United Kingdom. Fax: +44 113 343 3308.

1. There appears to be a large gap between the modelstructure presented in Figure 2 and what the authorsactually developed as the integrated model. An inte-grated model (such as that depicted in Figure 2) is asingle mathematical model, which is calibrated usingthe input and output data. In the present case, theinput data are rainfall and the output data is the flowat a future time step. The parameters of the inte-grated model proposed by the authors in Figure 2consist of the parameters of the semi-distributed con-ceptual model for the three sub-catchments and theweights of the ANN model performing the non-linear routing of flow to the outlet of the overallcatchment. Therefore, the calibration of the threesub-catchment parameters and the estimation of theoptimal set of ANN weight parameters should be car-ried out simultaneously to minimize the sum squareerror (SSE) at the output layer of the ANN. How wouldthe authors respond to the criticism that the resultspresented by the authors do not correspond to inte-grated model shown in Figure 2.

2. Ideally one would need the actual observed flowvalues at the outlet of the three sub-catchments tocalibrate the distributed conceptual model at the

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Comment on ‘‘Integration of artificial neural networks with conceptual models in rainfall-runoff modeling’’ 275

sub-catchment scale. It is not clear if such data wereavailable. In the absence of the flow values (Q1, Q2,and Q3), how the conceptual model parameters weredetermined? What error was minimized to estimatethe conceptual model parameters? Was it the SSE atthe overall outlet of the catchment (Q)? If yes, thenhow the three flows were super-imposed during cali-bration, linearly, or non-linearly as per Figure 2? Itappears that they were superimposed linearly to min-imize the SSE in estimating the total Q from the wholecatchment. If that is the case, then the objectiveslaid out in the introduction section do not appear tohave been fulfilled.

3. The manner in which the parameters of the concep-tual model were estimated may not be appropriate.The parameters of two sub-catchments were fixed(equal to their lumped counter-parts) and the param-eters of the third sub-catchment were optimized. Thisexercise was repeated three times to determine thesemi-distributed parameters from the three sub-catchments. Such a trial-and-error approach wouldnot result in an optimal set of parameters for thewhole catchment. As a result, the sub-catchmentparameters thus obtained are bound to contain largeerrors in them that will appear in the simulated flowsat the individual catchments. The ANN model trainedon such erroneous flows (Q1, Q2, and Q3) cannot beexpected to be significantly better than the concep-tual or simple ANN models, which is reflected in theresults presented by the authors. This is particularlycritical because when the performance of the lumpedconceptual model is good then the additional effortsof developing hybrid ANN models may not be justified.

4. The authors mention that (for SMAR model) in calcu-lating the actual evaporation depth, the catchmentis assumed to be the vertical stack of horizontal lay-ers, each of which contains 25 mm depth of soil mois-ture at field capacity except the bottom layer. Howmany such soil layers were needed for the catchmentunder consideration? How the depth of each layer wasdecided as 25 mm? If one were to use the depth as 20or 30 mm, would that impact the overall flow estima-tion from the whole catchment? If yes, how?

5. The authors state (page 11) that ‘‘since the hyper-bolic tangent and truncated linear functions causedtesting error to oscillate significantly, the logisticfunction was chosen. A mathematical model (includ-ing ANN hydrologic model) is developed by using theresults from the calibration/training data only andthe performance of the model is then evaluated usingthe testing data set. Using the testing results in decid-ing the ANN architecture or its components, e.g.,activation function is not appropriate.

6. How was the learning rate varied for each neuron, andfor each iteration? It is stated (on page 11) that thetesting error (training error) was set to 0.1, whichmeans that the testing error (training error) of theoutput was within 10%. Normally, the ANNs aretrained on SSE at the output layer. How an SSE of0.1 translates into the testing error being within 10%of the target flows? Further, the use of terms ‘‘train-

ing error’’ and ‘‘testing error’’ interchangeably isconfusing and not consistent with the ANNterminology.

7. The authors state that the ability of the systems the-oretic models, such as ANNs, to fit the data reason-ably very well cannot be taken seriously asvalidation of the physical interpretation ascribed tothese parametric forms, and they attribute suchremarks to O’Connor (1997). Such statements wereprobably true prior to 1997 but there have beenattempts that demonstrate the ability of the ANNhydrologic models in capturing the physics of theunderlying processes (Wilby et al., 2003; Jain et al.,2004; and Sudheer and Jain, 2004). Further, whenthe ANN hydrologic models are developed by consider-ing the physical processes in some sense; for example,selecting input based on the time of concentration(Jain and Indurthy, 2003); combining conceptual andANN techniques to develop an integrated concep-tual-ANN rainfall-runoff model (Jain and Srinivasulu,2004); or decomposing a flow hydrograph into differ-ent segments and modeling simpler pieces using asuitable technique (Jain and Srinivasulu, 2006); thenthe ability of ANN hydrologic models to fit the datavery well can be taken seriously as validation of thephysical interpretation ascribed to these parametricforms.

8. The performance of the integrated semi-distributedANN model is compared with semi-distributed regres-sion model. The regression model was linear in nat-ure. It is well established that ANNs are superior tosimple linear regression models due to their highlynon-linear parallel distributed nature. As such, itwould have been interesting to compare the inte-grated ANN models with the integrated-nonlinearregression models.

9. Looking at the verification results (Tables 4–6), thesemi distributed linear regression model performedpoorly for Xinanijiang and only marginally better thanthe lumped approach for SMAR and Tank models. Aspointed earlier, comparing integrated ANN modelwith a non-linear regression model would have beenmore interesting.

10. The authors mentioned that the increased number ofparameters does not result in a better model perfor-mance. This statement is not supported by the resultspresented by them. The number of parameters in thelumped forms of Xinanijiang, Tank, and SMAR modelsare 15, 12, and 9, respectively. The performance ofthe model with higher number of parameters is betterthan the one with less number of parameters as perthe results presented in Tables 4–6 (SMAR model dur-ing verification being the only exception). Further,the authors mention that the lumped Tank model con-tains a total of 12 parameters but the Table 3 showsonly 11.

11. The authors have used only two error statistics, SSEand R2, which is inadequate in the opinion of the dis-cusser in light of several studies stressing the need toemploy a wide variety of statistical measures in eval-uating the ANN hydrologic models.

276 A. Jain

12. Figure 7 shows that there are large variations betweenobserved flows and flows estimated from the lumpedmodels at all magnitudes. The integrated (ANN) modeldoes very well to reduce these variations at low mag-nitudes. However, the integrated ANN models werenot able to reduce such variations at high magnitudeflows significantly. This may be due to (a) the inabilityof the training method (back-propagation) to capturecomplex relationships of widely varying magnitudes,(b) non-optimal solutions for the conceptual modelcalibration, (c) ANN training based on flows from indi-vidual catchments computed using sub-optimal solu-tions, and/or (d) a combination of the above.Further, the scatter plots (Figure 7) are much betterway of assessing model performance than the timeseries plots (Figure 8).

The rainfall-runoff process is an extremely complex, dy-namic, non-linear and fragmented process that is not clearlyunderstood and is very difficult to model (Zhang and Gov-indaraju, 2000). The past experience has shown that a singletechnique is not capable of capturing such complexities andspecialized efforts are needed to achieve improved modelperformance. The efforts of the authors are indeed laudablesince they have been able to demonstrate that we need toshed the pre-conceived notions of using the available tech-nique in isolation. The research work presented by theauthors is an important step in developing efficient modelsof the hydrological process using an integrated approach. Aresponse from the authors to clarify certain issues pointed

out in this comment would not only help in better under-standing of the work presented but also increase the utilityof the authors’ work.

References

Jain, A., Indurthy, S.K.V.P., 2003. Comparative analysis of eventbased rainfall-runoff modeling techniques-deterministic, statisti-cal, and artificial neural networks. J. Hydrol. Eng. ASCE 8 (2), 1–6.

Jain, A., Srinivasulu, S., 2004. Development of effective andefficient rainfall-runoff models using integration of determinis-tic, real-coded genetic algorithms, and artificial neural networktechniques. Water Resour. Res. 40 (4), W04302. doi:10.1029/2003WR002355.

Jain, A., Srinivasulu, S., 2006. Integrated approach to modellingdecomposed flow hydrograph using artificial neural network andconceptual techniques. J. Hydrol. 317 (3–4), 291–306.

Jain, A., Sudheer, K.P., Srinivasulu, S., 2004. Identification ofphysical processes inherent in artificial neural network rainfallrunoff models. Hydrol. Process. 118 (3), 571–581.

O’Connor, K.M., 1997. Applied hydrology I-deterministic. Unpub-lished Lecture Notes, Department of Engineering Hydrology,National University of Ireland, Galway.

Sudheer, K.P., Jain, A., 2004. Explaining the internal behavior ofartificial neural network river flow models. Hydrol. Process. 118(4), 833–844.

Wilby, R.L., Abrahart, R.J., Dawson, C.W., 2003. Detection ofconceptual model rainfall-runoff processes inside an artificialneural network. Hydrol. Sci. J. 48 (2), 163–181.

Zhang, B., Govindaraju, S., 2000. Prediction of watershed runoffusing Bayesian concepts and modular neural networks. WaterResour. Res. 36 (3), 753–762.