International Journal of Research in Engineering and Social Sciences ISSN 2249-9482, Impact Factor: 5.343, Volume 5 Issue 5, May2015 Website: www.indusedu.org

RAINFALL RUNOFF MODELING OF RIVER BASIN USING ANN –A CASE STUDY Subha Sinha Asst. Professor, Dept. of Civil Engineering, B.I.T. Mesra, Campus, Patna V. Singh Professor, N.I.T., Patna Campus M. P. Jakhanwal Professor, ABESIT, Noida

ABSTRACT In this paper, rainfall-runoff models of Punpun river basin have been developed using the ANN Technique. Punpun River carries huge water during monsoon and discharges in the river Ganga at in downstream of Patna. Some part of this basin is inundated during monsoon, which creates lot of problems to the people who live nearby. This monsoon water may be utilized in the lean season by conserving it on the upstream side of the River, Punpun. Keeping this in view, this study has been carried out. For this purpose a three-layered feed forward network structure with back propagation algorithm was used to train the ANN model. The monthly and seasonal rainfall and runoff data from 1990 to 2010 of Punpun river basin has been considered in this study. The runoff data have been considered at Sripalpur gauging site. Performance evaluation of the model has been carried out using statistical parameters. Two sets of data have been used to make several combination of year keeping in view the highest peaks of hydrographs. It was found that the first set of data gave better result than second set of data. The study also demonstrates the applicability of ANN approach in developing effective non-linear models of Rainfall-Runoff process without the need to explicitly representing the internal hydraulic structure of the Punpun basin. Key Words: Rainfall-Runoff, Modelling, ANN Techniques, Punpun River Basin.

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International Journal of Research in Engineering and Social Sciences ISSN 2249-9482, Impact Factor: 5.343, Volume 5 Issue 5, May2015 Website: www.indusedu.org

INTRODUCTION The rainfall-runoff process is an extremely complex, dynamic, non-linear, time varying, spatially distributed, and not easily described and is very difficult to model. Runoff is one of the most important hydrologic variables used in most of the water resources application. Reliable prediction of quantity and rate of runoff from land surface into stream or river is difficult and time consuming too. Considerable amount of research effort in the area of hydrology during the past few decades has been devoted towards development of computer based models of rainfall-runoff processes. A rainfall-runoff model is used to simulate the hydrologic response of a catchment to rainfall input. The estimation of runoff from a catchment is required for the purposes such as design of storage facilities, to assess the flood, assessment of water available for municipal, agricultural or industrial purposes, planning irrigation operations, estimating future dependable water supplies for power generation, wild life protection etc. Many rainfall-runoff models have been developed over the years. These models can be broadly divided in three categories: Black box models, Conceptual models and physically based models. The Black box models are based on transfer functions which relate inputs with outputs and generally do not have any physical basis. The success of these models can be attributed mainly to simple mathematics, minimum computational requirements and acceptable results. Conceptual models require large computation for calibrating the parameters involved. Application of distributed models requires large quantity of data compared to lumped models and large computer resources for successful implementation. The time required to construct these models is enormous and thus an alternative modeling technique is needed when detailed modeling is not required. All these models, however, require detailed knowledge of a number of factors and initial boundary conditions in a catchment area which in most cases are not readily available. However, the significant data requirements of such models, coupled with the time involved in the model development, calibration and validation compared to other model categories, make them an unfavorable choice in operational hydrology. The linear time series models such as ARMA (Auto Regressive Moving Average) have been developed to handle such situations because they are relatively easy to implement. In recent years, Artificial Neural Networks (ANNs) have become very popular for prediction and forecasting in a number of areas including finance, power generation, medicine, water resources and environmental science. The main reason is that ANNs can represent any E-mail id:- [email protected] Page 33

International Journal of Research in Engineering and Social Sciences ISSN 2249-9482, Impact Factor: 5.343, Volume 5 Issue 5, May2015 Website: www.indusedu.org

arbitrary nonlinear function given sufficient complexity of the trained neural network (Dawson and Wilby, 1998). ANNs can find relationship between different input samples and can group samples in similar way to cluster analysis. ANNs are able to generalize a relationship from small sample of data, are robust in the presence of noisy or missing inputs and can learn in response to changing environments. ANNs have been applied widely in various aspects of hydrology such as rainfall-runoff modelling, stream flow forecasting, ground water modeling, water quality, water management policy, precipitation forecasting, hydrological time series, and reservoir operations (ASCE, 2000a). ASCE (2000a, 2000b) reported the applications of ANN in hydrology and water resources. ANN models provided better results when compared with other conceptual SAC-SMA (Sacramento soil moisture accounting) model (Hsu et al., 1995), autoregressive models (Raman and Sunilkumar, 1995), ARMAX model (Fernando and Jayawardena, 1998), Volterra type Functional Series Model (Sajikumar and Thandaveswara, 1999), multiple regression models (Thirumalaiah and Deo, 2000), linear and non-linear regressive model (Elshorbagy et al., 2000), and Conceptual models (Tokar and Markus, 2000). Sudhir et al. (2001), Kumar et al. (2008), Kaltech (2008), Solaimani (2009), Nourani et al. (2011), Nourani et al. (2014); Asadnia et al. (2014) have used the ANN model for the rainfall-runoff studies. Sudhir et al. (2001), used ANN technique with back propagation algorithm for the development of rainfall - runoff model. The statistical properties of data series such as auto, partial and cross correlation values were used to select and appropriate input vector for the model development. Kumar et al. (2008) examined the effectiveness of the rainfall - runoff modeling with ANNs by comparing their results with AREVIA model and concluded that ANN could provide more accurate discharge forecasts than the traditional mentioned model. Kaltech (2008) has introduced the interpretation diagram, Garson's algorithm, and randomization approaches to understand the relationship learned by ANN model. The results indicated that ANNs are promising tools not only in accurate modeling of complex processes but also in providing insight from the learned relationship. Solaimani (2009) has demonstrated the application of the feed forward back propagation for the rainfall forecasting with various algorithms with performance of multi-layer perceptions. Nourani et al. (2011) used ANN for hybrid wavelet genetic programming (WGP) approach to optimize ANN modeling of rainfall - runoff process and found that the results of the WGP and WGPNN (wavelet genetic programming neural network) model are more E-mail id:- [email protected] Page 34

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satisfactory with respect to the GP and ANN models in terms of prediction accuracy by considering a multi resolution concept in the modeling. Nourani et al. (2014) has used ANN to study the signature of hysteresis phenomena in hydrological processes for the Eel River watershed. Authors concluded that ANN efficiently considers hysteresis signs when modeling hydrological processes. Asadnia et al. (2014) has used the particle swarm optimization (PSO) technique for training an artificial neural network to predict water levels and compared the results with Levenberg-Marquardt neural network (LM), Conjugate gradient (CG) and gradient descent (GD) algorithms. Authors concluded that LM algorithm gave the best results compared to GD and CG algorithms but the PSO based ANNs were superior to the LM based ANN model. Rajurkar et al.(2002), Tayfur and Singh (2006), S M Chen et al. (2013), have used the ANN model for the flood estimation. Rajurkar et al. (2002) applied ANN for modelling daily flows during monsoon flood events for a catchment in using daily rainfall data as input vector of the network model. Tayfur and Singh (2006) used three–layered feed forward neural network using sigmoid function with back propagation algorithm to forecast the runoff and compared with fuzzy inference method. S M Chen et al. (2013) used ANN technique with feed forward Natural network with back propagation algorithm for runoff estimation and compared with Conventional Regression Analysis (CRA). They found that Feed Forward Back Propagation network (FFBP) gave superior result than Conventional Regression Analysis (CRA). The results of any model application depend upon the quality of input data. In undeveloped and developing countries, one frequently encounters a situation wherein the input data are of poor quality and inconsistent. Typically rain gauge network is inadequate which means that the spatial variation of rainfall is poorly represented. Enough secondary information may not be available to improve the quality of input data or to remove inconsistency. Nevertheless, modeling has to be carried out for a variety of purposes such as river basin planning, hydrologic design of projects, flood forecasting, etc. ANN models are built using the input and output observations without the detailed understanding of the complex physical laws governing the process under investigation. It is also able to provide reasonably accurate model for the process under investigation, as a large number of the applications in hydrology along with the comparison of their predictive performance with other methods (Kaltech, 2008). The results of various ANN models indicate that ANNs can be powerful E-mail id:- [email protected] Page 35

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tools in modeling the rainfall-runoff process for various time scale, topography, and climatic patterns. The objective of this study is to develop a rainfall-runoff model for Punpun river basin using Artificial Neural Networks (ANNs) Technique. Three-layered feed forward network (FFN) structure is used to construct the model. The back propagation algorithm is used to train the ANN model. The monthly and seasonal rainfall and runoff data of Punpun river basin are considered for the development of monthly and seasonal rainfall-runoff model using ANN technique. The performance of the model has been evaluated using various statistical indices. STUDY AREA AND DATA USED The river Punpun originates in hills of the Palamau district which falls in the state known as Chotanagpur region at an elevation of about 300 m and at Latitude of 24° 11’ N and Longitude 84° 9' E. It joins the river Ganga near Fatuha, about 25 km downstream of Patna () covering total distance of 232 km. The Punpun basin lies between latitudes 24° 11' to 25° 25' N and longitudes 84 ° 9' to 85° 20' E. It is located on the right bank of the river Ganga (as shown in Figure 1 with red colour) and is bounded by the Sone river system on its west and Kiul-Harohar-Falgu river system in the east. The basin is roughly trapezoidal in shape. The total catchment area of the basin is about 8,530 km2. This is 1% of the total area of Ganga basin in the country. The general drainage direction of the basin is from south-west to north east.

Figure 1: Punpun sub-basin map in Ganga river basin

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The uppermost catchment of Punpun basin falls in the districts of Palamau and Hazaribagh in Chotanagpur hills is mostly covered under forest. The lower part lies in the districts of Aurangabad, Gaya and Patna are having mild slopes. The elevation varies from 300 m near origin and about 50 m at its outfall into the river Ganga. Punpun River basin contributes huge amount of water in river Ganga during monsoon but remaining season lives dry. Some part of this basin is inundated during monsoon, which creates lot of problems to the people who live nearby. This monsoon water may be utilized in the lean season by conserving it on the upstream side of the River, Punpun. For this study, monthly rainfall data of 21 years i.e. from 1990 to 2010 at five different rain gauge stations namely Nabinagar, Gurva, Goh, Jahanabad and Patna in Punpun river basin as shown in Figure 2, have been used. Theisen polygons were drawn using these rain gauge stations to compute the average depth of monthly rainfall over the basin. The discharge data measured at Sripalpur gauging site by Central Water Commission (Central Water Commission), Patna was used.

Figure 2: Map showing location of selected rain gauge stations and selected discharge site of the study area These monthly rainfall and runoff data were used to calibrate and validate the ANNs monthly

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model. The complete data is divided in two parts, part one is, approximately 70 %, which is used for the calibration and remaining data 30 % is used for the validation of the model. Part one data is from 1990 to 2004 for the calibration and part two data is from 2005 to 2010 for validation. Seasonal data have also been generated by considering four seasons in a year. These four seasons are: pre-monsoon (March, April and May), monsoon (June, July, August, and September), post-monsoon (October and November) and winter (December, January and February). METHODOLOGY Artificial Neural Network (ANN) Model: An ANN model consists of number of layers and nodes that are organized to a particular structure. There are various ways to classify a neural network. The classification of neural networks is done by the number of layers, connection between the nodes of the layers, the direction of information flow, the non-linear equation used to get the output from the nodes, and the method of determining the weights between the nodes of different layers. The commonly used neural network is three-layered feed forward network due to its general applicability to a variety of different problems (Hsu et al., 1995), presented in Figure 3. The first layer is the input layer and its role is to pass the input variables onto the subsequent layers of the network. The last layer consists of the output variables and is called as output layer. The layer(s) in between are called as hidden layer(s) and the introduction of this layer enhances the network’s ability to model complex functions. The processing elements in each layer are called nodes. The information flow and processing in this network is from input layer to hidden layer and from hidden layer to output layer. The number of nodes in input and output layers is decided by the problem to be addressed. The number of hidden layers and the number of nodes in each hidden layer are problem dependent and are usually determined by a trial and error procedure. A synaptic weight is assigned to each link to represent the relative connection strength of two nodes at both ends in predicting the input-output relationship. The output, yj of any node j, is given as:

m

y j f Wi X i b j (1) i 1

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International Journal of Research in Engineering and Social Sciences ISSN 2249-9482, Impact Factor: 5.343, Volume 5 Issue 5, May2015 Website: www.indusedu.org

Input layer Hidden layer Output layer

Network Input Network Output X Y

Figure 3: A Typical Three-Layer Feed Forward ANN where Xi is the input received at node j, Wi is the input connection pat way weights, m is the total numbers of inputs to node j, bj is the node threshold and function f is called an activation function. It determines the response of a node to the total input signal it receives and is given as the sigmoid function (Dawson and Wilby, 1998) 1 f (x) (2) 1 e x The characteristics of a sigmoid function are that it is bounded above and below, it is monotonically increasing, and it is continuous and differentiable everywhere. Any nonlinear process can be mapped using this sigmoidal function (ASCE, 2000a). Back propagation, the most popular algorithm used for the training of the Feed Forward ANNs by Hsu et al., (1995) and Burian et al., 2001), is used for training the ANN. In this process, each input pattern of the training data set is passed through the network from the input layer to output layer. The network output is compared with the desired target output, and an error is computed as

E y t 2 (3) i i P p

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where ti is a component of the desired output T; yi is the corresponding ANN output; p is the number of output nodes; and P is the number of training patterns. This error is propagated backward through the network to each node, and correspondingly the connection weights are adjusted based on the equation (ASCE, 2000a) E wij n wij n 1 (4) wij where wij (n)and wij (n 1) are weight increments between node i and j during nth and (n- 1)th pass. Due to the boundation of the sigmoid function between 0 and 1, all input values should be converted to the range between 0 and 1 before passing into a neural network (Smith and Eli, 1995). The output from the ANN should be denormalized to provide meaningful results. Equation used to normalize the data set is: R N i (5) i (Max a) (Min b) i i

Where, Ri is the real value applied to node i; Ni is the subsequent standardized value calculated for node i; Mini is the minimum value of all values applied to node i; Maxi is the maximum value of all values applied to node i. a and b are constants to fix the range of normalization. MODEL PERFORMANCE EVALUATION CRITERIA: The performance of a model can be evaluated in terms of several characteristics. Root mean square error (RMSE) and coefficient of correlation (R) are the numerical performance indicators used to compare the models. They are defined as follows: TY Coefficient of correlation (R) (6) T 2 Y 2

K t y 2 RMSE k 1 (7) K where, K is the number of observations; t is the observed data; y is computed data; T t t in which t is the mean of the observed data; and Y y y in which y is the mean of the computed data. TRAINING OF THE FEED FORWARD NETWORK MODEL: E-mail id:- [email protected] Page 40

International Journal of Research in Engineering and Social Sciences ISSN 2249-9482, Impact Factor: 5.343, Volume 5 Issue 5, May2015 Website: www.indusedu.org

The network was trained initially considering rainfall at the current time, t as input vector and runoff as output vector. This combination was trained with initial value of error tolerance, learning parameter, the number of cycles for learning and the neurons in the hidden layer. The output values from the network were denormalized and compared with the observed targeted values. The performance criteria, root mean square error (RMSE) and coefficient of correlation (CC) were used to examine the performance of the model. For this initial combination, the root mean square error of targeted and expected values was very high, 5442 and coefficient of correlation was low, 0.636. Then the network was trained with the decreased values of error tolerance and varied values of the learning parameter. The learning parameter and the error tolerance were fixed with low root mean square error and high coefficient of correlation. The neurons in the hidden layer were increased from minimum to the number from where the coefficient of correlation decreases. The number of neurons, which gave highest coefficient of correlation, was selected for this combination. To get the optimized structure for the neural network model, the various combinations of inputs were trained and it was found that the best combination was rainfall(t-3), rainfall(t-2), rainfall(t-1) and rainfall(t) as input and runoff(t) as output. The error tolerance, the learning parameter, the number of cycles and neurons in the hidden layer were 0.001, 0.4, 3000 and 8 respectively. For this combination the RMSE and CC were satisfactory. The weights for the best-trained network structure were collected from the training module of the Back Propagation (BP) simulator and these weights were frozen to evaluate the trained network. The monthly rainfall and runoff data were normalized and the data set of input vector was prepared according to the best trained neural network structure. The runoff was computed using this network and the weight vector for this trained network structure. The computed runoff values were denormalized and compared with the observed runoff values. RESULTS AND DISCUSSION In this study, Monthly and Seasonal models have been developed depending up on the data used i.e. for Monthly Model, mean monthly data of rainfall and runoff have been used and for Seasonal Model, seasonal data of rainfall and runoff have been used. For each model, two sets of data have been used for different combination of years. First set of data used is from 1990 to 2004 for the calibration and from 2005 to 2010 for validation. The second set of data is from 1996 to 2010 for calibration and from 1990 to 1995 for validation. Monthly Model: E-mail id:- [email protected] Page 41

International Journal of Research in Engineering and Social Sciences ISSN 2249-9482, Impact Factor: 5.343, Volume 5 Issue 5, May2015 Website: www.indusedu.org

First of all, the rainfall and runoff data from 1990 to 2004 have been used for the calibration of the monthly model. Figure 4 presents the calibrated results along with the observed. It can be seen from the Figure 4 that all the peaks of the computed runoff hydrograph were not matching well with the observed peaks, but the pattern of both the hydrographs matches well. For this case the coefficient of correlation was 0.936 and RMSE was 1137. The coefficient of correlation was good, but the root mean square error was large. The model was validated with data from 2005 to 2010. Validated results have been shown in Figure 5. Again all the peaks of the computed hydrograph are not matching well with the observed. The coefficient of correlation was 0.822 and RMSE was 1906. CC is satisfactory but RMSE is high. From the observed data it can be seen that the highest peaks are not matching well in the validation, So the second set of data have been prepared, in which the data from 1996 to 2010 have been used for calibration and remaining data from 1990 to 1995 have been used for validation purpose.

Figure 4: Calibrated Results of the best combination of ANN for 1990 to 2004 Figure 6 compares the computed runoff hydrograph with the observed for the calibration of the model. It can be seen from Figure 6, that all the computed peaks are again more or less same as the observed peaks of runoff hydrograph and there are no time lags for the occurrence of the peaks. The coefficient of correlation between the observed and computed runoff was 0.913 and root mean square error was 1343 for calibrating the network.

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Figure 5: Validation result of Runoff by ANN model for 2005 to 2010 The coefficient of correlation is again good, but the RMSE is large. Figure 7 presents the computed and observed hydrograph for the validation of the model for the data of the year 1990 to 1995. It can be seen from Figure 7 that not a single peak is matching with the observed peak, but the nature of the computed runoff hydrograph is matching well with the observed runoff hydrograph. The CC is 0.873 and RMSE is 1382.

Figure 6: Calibrated results of the best combination of ANN for 1996 to 2010

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Figure 7: Validation result of runoff by ANN model for 1990 to1995 By comparing the results from the above two sets of data, it is found that the results obtained from the first set of data are better than the second set of the data. Though the options were tried but the monthly model is selected of the first set of data with the optimal weights. Analyses of above results indicate that the developed ANN model does not provide very satisfactory results for validation period. This problem may be due to the short span of data used for the calibration, some changes in the catchment and some errors present in the input data itself. Seasonal Model: The same procedure was repeated for the seasonal model. In this model, seasonal data of rainfall and runoff were used to calibrate and validate the seasonal model. First of all, data from 1990 to 2004 have been used for calibration and from 2005 to 2010 for validation. Figure 8 presents the computed and observed runoff. It can be seen from Figure 8 that all the computed peaks are more or less same as the observed peaks except few. The CC was 0.969 and RMSE was 1926 for calibrating the network. The CC is good but the RMSE is high. Figure 9, presents the observed and computed runoff for the validation of the seasonal model. The seasonal rainfall and runoff data of the years 2005 to 2010 were used for this purpose. It can be seen from Figure 9 that all the peaks of the computed runoff data are not matching with the observed peaks. In this figure the first two computed peaks are high in comparison to observed peaks and the third computed peaks is lower than the observed peaks. The CC was 0.953 and RMSE was 3301, which is very high. It is observed that the matching is not good in the validation period. Therefore, another set of data have tried from 1996 to 2010 for E-mail id:- [email protected] Page 44

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calibration and remaining period 1990 to 1995 were used for validation purpose.

Figure 8: Calibrated Results of the best combination of ANN for 1990 to 2004

Figure 9: Validation result of Runoff by ANN model for 2005 to 2010 Figure 10, presents the computed runoff hydrograph for the calibration period in second set of data. It can be seen from Figure 10, that all the computed peaks are again more or less same as the observed peaks of runoff hydrograph and there are no time lags for the occurrence of the peaks. The CC was 0.984 and RMSE was 1680 for calibrating the network. The coefficient of correlation is again very good and RMSE is also less. Figure 11 presents the observed and computed Runoff for the validation model for the data from 1990 to 1995. It can be seen from figure that most of the peaks of the computed runoff hydrograph are more or less same as observed peaks. The CC is satisfactory (0.920) whereas RMSE is high (2695)

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Figure 10 : Calibrated Results of the best combination of ANN for 1996 to 2010

Figure 11: Validation result of Runoff by ANN model for 1990 to1995 By comparing the results from the above two sets of seasonal data, it is found that the results obtained from the first set of data are better than the second set of the data. Though the options were tried but the seasonal model is selected of the first set of data with the optimal weights. CONCLUSION In this study, ANN models for the rainfall-runoff processes were developed for Punpun river basin. Three layered feed forward network structure was used to model the process. Back propagation algorithm was used to train the ANN model. Fifteen different combinations of rainfall and runoff were considered as input to the network and trained by BP algorithm with

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different error tolerance, learning parameter, number of cycles and number of hidden layers. The best combination compared to other combinations has been selected with high value of coefficient of correlation, 0.936 and low root mean square error, 1137. Though the validation of the model did not give as good result as in the case of the training. For seasonal model, again the best combination were selected for which the coefficient of correlation is 0.969 and root mean square error is 1926. The validation of the model did not give as good result as in the case of the training. In the training of ANN models, the main objective was to achieve a global minimum error on the whole length of the data. Training the model with long record of data, which contain more extreme events, can reduce the large variations in the statistical parameter. It was observed from the training and validation results that ANNs are good for studying the underlying process in rainfall runoff relationship. These models can be used to simulate the different scenarios of input as rainfall and the runoff can be estimated. The study demonstrates the applicability of ANN approach in developing effective non-linear models of Rainfall-Runoff process without the need to explicitly representing the internal hydraulic structure of the watershed. REFERENCES: 1. ASCE (2000a). “Artificial neural networks in hydrology-I: Preliminary concepts.” Journal of Hydrologic Engineering, ASCE, 5(2), 115-123 2. ASCE (2000b). “Artificial neural networks in hydrology-II: Hydrologic applications.” Journal of Hydrologic Engineering, ASCE, 5(2), 124-137. 3. Asadnia, Chuo, L.H.C., Qin, X. S. ASCE, AM and Talei, Amin (2014), “Improved Particle Swarm Optimization-Based Artificial Neural Network for Rainfall-Runoff Modelling”, Journal of Hydrologic Engineering, ASCE, 19(7), 1320-1329. 4. Burian, S.J., Durrans, S.R., Nix, S.J. and Pitt, R.E. (2001). “Training artificial neural networks to perform rainfall disaggregation.” Journal of Hydrologic Engineering, ASCE, 6(1), 43-50. 5. Dawson,C.W., and Wilby, R. (1998). “An artificial neural network approach to rainfall- runoff modeling.” Hydrological Sciences Journal, 43(1), 47-66. 6. Elshorbagy, A., Simonovic, S.P. and Panu, U.S. (2000), “Performance evaluation of artificial neural networks for runoff prediction.” Journal of Hydrologic Engineering, 5(4), 424-433.

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7. Fernando, A.K. and Jayawardena, A.W. (1998). “Runoff forecasting using RBF networks with OLS algorithm.” Journal of Hydrologic Engineering, ASCE, 3(3), 203-209. 8. Hsu, K.L., Gupta, H.V. and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resources Research, 31(10), 2517-2530. 9. Kaltech ,M.A.(2008)"Rainfall-Runoff Modeling Using Artificial Neural Networks(Ann's) modeling and understanding" Caspian Journal of Environmental Sciences ,Vol .6 No.l 53-58. 10. Kumar R.P.,Ramanda.v.m.,Eashwer,D. and Venkaldas,M. (2008)"TimeSeries Modeling Using Artificial Neural Networks" Journal Of Theoretical and applied information Technology JatiU259-1264. 11. Nourani, V., Kisi, O., and Komasi, M. (2011). “Two hybrid artificial intelligence approaches for modeling rainfall–runoff process.” J. Hydrol., 402(1–2), 41–59. 12. Nourani, V., Parhizkar, M., Daneshvar, F.V., and Amini B, (2014), “Capability of Artificial Neural Network for Detecting Hysteresis Phenomenon Involved in Hydrological Processes”, Journal of Hydrologic Engineering, 19(5), May, 896-906. 13. Raman, H., and Sunilkumar,N. (1995). “Multivariate modelling of water resources time series using artificial neural networks.” Hydrological Sciences Journal. 40(2), 145-163. 14. Rajurkar, M. P., Kothyari C and Chaube U C, (2002), “Artificial neural networks for daily rainfall–runoff modelling”; J. Hydrol. Sci. 47 865–877. 15. Sajikumar, S., and Thandaveswara, B.S. (1999). “A non-linear rainfall-runoff model using an artificial neural network.” Journal of Hydrology, 216, 32-55. 16. Smith, J., and Eli, R. N. (1995). “Neural network models of rainfall-runoff process.” Journal of Water Resources Planning and Management, ASCE, 121(6), 499-508. 17. Solaimani,K.(2009)"Rainfall-Runoff Prediction Based On Artificial Neural Network;A case study Jarahi watershed" American-Eurasian Journal of Agric. & Environ.Sci 5(6):856-865. 18. Sudheer et al (2001). “Selection of appropriate input vector to neural network based rainfall-runoff models: A statistical approach.” International Conference on Civil Engineering, Bangalore, July 2001, 464-471. 19. S M Chen, Y M Wang and I Tsou (2013), “Using artificial neural network approach for modelling rainfall–runoff due to typhoon” J. Earth Syst. Sci. 122, No. 2, April, pp. 399– 405 E-mail id:- [email protected] Page 48

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20. Tayfur G and Singh V.P., F.ASCE (2006) “ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff” Journal of Hydraulic Engineering, Vol. 132, No. 12, ASCE, ISSN 0733-9429/2006/12-1321–1330. 21. Thirumalaiah, K., and Deo, M.C. (2000). “Hydrological forecasting using neural networks.” Journal of Hydrologic Engineering, ASCE, 5 (2), 180-189. 22. Tokar, A.S. and Markus, M. (2000). “Precipitation-Runoff modeling using artificial neural networks and conceptual models.” Journal of Hydrologic Engineering, ASCE, 5(2), 156-161.

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