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RAINFALL RUNOFF MODELING of PUNPUN RIVER BASIN USING ANN –A CASE STUDY Subha Sinha Asst

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RAINFALL RUNOFF MODELING of PUNPUN RIVER BASIN USING ANN –A CASE STUDY Subha Sinha Asst 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 PUNPUN RIVER BASIN USING ANN –A CASE STUDY Subha Sinha Asst. Professor, Dept. of Civil Engineering, B.I.T. Mesra, Patna 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 Fatuha 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. E-mail id:- [email protected] Page 32 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 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 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 India 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.
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