 Open Access
 Total Downloads : 255
 Authors : Lateef Ahmad Dar
 Paper ID : IJERTV6IS040083
 Volume & Issue : Volume 06, Issue 04 (April 2017)
 DOI : http://dx.doi.org/10.17577/IJERTV6IS040083
 Published (First Online): 01042017
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Identification of the Input Vector for RR Modelling of River Jhelum Catchment
Lateef Ahmad Dar
National Institute of Technology, Srinagar, J&K.
Abstract: Hydrologic engineering design and management purposes require information about runoff from a hydrologic catchment. In order to predict this information, the transformation of rainfall on a catchment to runoff from it must be modelled. One approach to this modelling issue is to use empirical RainfallRunoff (RR) models. Artificial neural networks (ANNs) are among the most sophisticated empirical models available and have proven to be especially good in modelling complex systems. Their ability to extract relations between inputs and outputs of a process, without the physics being explicitly provided to them, theoretically suits the problem of relating rainfall to runoff well, since it is a highly nonlinear and complex problem. The goal of this investigation was to develop rainfallrunoff models for the river Jhelum catchment that are capable of accurately modelling the relationships between rainfall and runoff in a catchment. It is for this reason that ANN and MLR techniques were tested as RR models on a data set from the upper Jhelum catchment in Jammu and Kashmir, India. For modeling the rainfallrunoff process in river Jhelum ,the input i.e. the precipitation was determined by taking the data from three rainguage stations viz. Srinagar, Pahalgam and Qazigund for years 20012013. The runoff data was taken for padshahibagh guaging station that lies in Srinagar, summer capital of Jammu and Kashmir for the years 20012013. From the predicative analysis , it was found that the flow at the Padshahibagh on any given day is dependent on previous three day flows .The number of previous day rainfall inputs influencing the discharge was determined by the trial and error method, the number of previous day rainfall inputs were increased from one to five. The comparison was based on various statistical parameters like root mean square error (RMSE) and R2.
Keywords: Rainfall; Runoff; Multiple linear regression (MLR) and Artificial neural network (ANN) technique.

NTRODUCTION
RainfallRunoff relationship remains one of the most complex processes in hydrological analysis. Rainfall runoff modelling is a very important tool for determining runoff generated for a particular amount of rainfall over the catchment. The response of a catchment to any amount of rainfall occurring over it is influenced by the precipitation characteristics as well as the catchment characteristics. There are a lot of things that influence the response of the catchment like rainfall depth, rainfall intensity, temperature , windvelocity, slope of the catchment, drainage density, type of soil, vegetative cover, topography etc. to determine the exact influence of these parameters on the overall response of the catchment is a very complex and cumbersome phenomena. We have to find and incorporate only those parameters which have a considerable influence on the response of the catchment and hence save the time spent on analysing the nonimportant variables. To find the parameters that influence the output considerably, we have to use the predicative analysis technique. The present study involves determining the inputs that are to be used in modelling the rainfallrunoff process of river Jhelum
catchment using blackbox techniques Viz. Multiple linear regression (MLR) and Artificial neural network (ANN) technique.

STUDY AREA
The present study was done on the upper Jhelum catchment. The study area spatially lies between 33Â° 21 54 N to 34Â° 27 52 N latitude and 74Â° 24 08 E to 75Â° 35 36 E longitude with a total area of 8600.78 sq.kms (Fig.1). It covers almost all the physiographic divisions of the Kashmir Valley and is drained by the most important tributaries of river Jhelum. Srinagar city which is the largest urban centre in the valley is settled on both the sides of Jhelum River and is experiencing a fast spatial growth. Physical features of contrasting nature can be observed in the study area that ranges from fertile valley floor to snow clad mountains and from glacial barren lands to lush green forests. Based on the consistency of data, the precipitation data was taken from three raingauge stations in the catchment viz. Srinagar, Qazigund and Pahalgam. The discharge was taken at Padshahibagh gauging station. The study area can broadly be divided into the following physiographic divisions viz Mountainous region of Pir Panjal and Greater Himalayas,The lacustrine deposits of Karewa , Jhelum Valley Floor. The geological history of the study area ranges from Cambrian to Recent. The central alluvial part of the Upper Jhelum catchment is a Recent formation surrounded by Karewas on the south and south west and Jurassic formations on the north northeast and northwest. These three are the major formations found in the study area. South eastern part of the study area is composed of Triassic and Cambro Silurian formations. Few linear stretches in the north of the study area are of Triassic and Jurassic formations interspersed with unclassified granites and gneisses. The Jhelum and its associated streams that drain the bordering mountain slopes together constitute the drainage network of the study area. They include the fairly developed systems of the Sind, Rembiara, Vishaw and Lidder rivers as well as tiny rivulets such as the Sandran, Bringi and Arapat Kol . Adjusted to the varying nature of geomorphic and geological setting, the fluvial systems in the study area have peculiar characteristics of their own. Drainage system of the Upper Jhelum catchment has an evolutionary history marked by stupendous changes in level, rejuvenating at one time, and at others becoming sluggish, or even choking their channels with their own debris with consequent diversions and the everthreatening process of mutual piracy.
Figure.1:Study area (Source: Generated from SOI toposheets, 1961)

IDENTIFICATION OF THE INPUT VECTOR
Identification of the number of flow series was carried by the predicative analysis. The selection of the predictors was carried out on the basis of ptest.
Figure 2: Predictative analysis for the number of input flow series.
The predicative analysis suggests incorporating flow values with three days lag in the input vector to the network. Table 1 shows the results obtained from the predicative analysis.
Table 1: Results of PTest.
Parameter
Pvalue
Intercept
0.327011116
Q(t1)
0.001728645
Q(t2)
0.005002098
Q(t3)
0.008978154
Q(t4)
0.079794358
Q(t5)
0.225577479
Q(t6)
0.824148923
Q(t7)
0.638607708
Q(t8)
0.584458284
Q(t9)
0.696624304
Q(t10)
0.505192364
Q(t11)
0.94723135
Q(t12)
0.760036923
Q(t13)
0.472803194
Q(t14)
0.106974693
The pvalue of Q(t1), Q(t2), Q(t3) is less than 0.05 while all other flow inputs from Q(t4) onwards exceed Pvalue of 0.05 and hence fail this test. So previous three day flows will influence the prsent day flow at PadshahiBagh. So previous three day flows values are to be put in the input vector.
Hence the predicative analysis suggests incorporating flow values with three days lag in the input vector to the network.
Figure 3: Observed vs. predicted discharge : input P(t1).
Figure.4: Observed vs. predicted discharge input P(t1)P(t5).
Table 2: Statistics of MLR.
INPUT
R2
MSE
RMSE
p(T1)
0.764
2.435
1.560
p(T1) p(T2)
0.818
1.474
1.214
p(T1)p(T3)
0.823
0.934
0.967
p(T1) p(T4)
0.825
0.465
0.681
p(T1) p(T5)
0.827
0.234
0.483
The RMSE and R2 values improve considerably on increasing the number of rainfall inputs from one to five.
0.84
0.82
0.8
0.78
0.76
0.74
0.72
p(T1)
p(T1) p(T2)
p(T1)p(T3)
p(T1) p(T4)
p(T1) p(T5)
RSquare
Figure 5: Variation of R2 with different input vectors in MLR
3
2.5
2
1.5
1
0.5
p(T1)
p(T1) p(T2)
p(T1)p(T3)
p(T1)..p(T4)
p(T1)..p(T5)
0
MSE
Figure 6: Variation of RMSE with different input vectors in MLR.
Table 3: Goodness of fit for the effect of no. of previous day input rainfall parameters.
NETWORK
INPUTS
RMSE
RSQUARE
Back propagation network 6 neurons.
P(t1)
0.521
0.776
P(t1)P(t5)
0.245
0.856
Back propagation network 10 neurons.
P(t1)
0.342
0.814
P(t1)P(t5)
0.132
0.891
The RMSE error improves when the number of input rainfall patterns in the input vector is increased from one to five. The first case considered 4 variables in the input
vector while the second considered eight. The above results conclude that the number of previous rainfall data has a significant effect on the model performance. Hence the input vector with 1, 2, 3, 4, 5 day lag can produce the river flow patterns in a satisfactory manner.
Table 4: Statistical indices of RBF model for various inputs.
NETWORK
INPUTS
RMSE
RSQUARE
RBF
P(t1)
0.097
0.912
P(t1)P(t5)
0.046
0.937
The statistical analysis shows that the performance of this network increases as the number rainfall inputs increase from one to five i.e. from p(t1) to p(t1)….p(t5).

CONCLUSIONS
From the predicative analysis it can be concluded that the discharge at PadshahiBagh is dependent upon previous three days flow. The number of previous day rainfall inputs affecting the runoff was determined by hit and trial method and it was observed that the models performed better when the number of rainfall inputs were increased from one to five i.e. from p(t1) to p(t1)……p(t5).The total number of input parameters vary from four to eight when the number of previous day rainfall inputs are varied from one to five respectively as previous three day flows are always to be provided in the input vector.

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