JECET; June – August-2013; Vol.2.No.3, 721-729.
Journal of Environmental Science, Computer Science and Engineering & Technology
An International Peer Review E-3 Journal of Sciences and Technology
Available online at www.jecet.org Environmental Science
Research Article
JECET; June – August 2013; Vol.2.No.3, 721-729. 721
Forecasting of Rain Fall in Varanasi District, Uttar Pradesh Using Artificial Neural Network
Rajan Kumarand G.S. Yadav
Department of Geophysics, Banaras Hindu University, Varanasi, India Received: 7 July 2013; Revised: 24 July 2013; Accepted: 29 July 2013
Abstract: Artificial Neural Network (ANN) is one of most importance modern tool used for data processing, forecasting in a rain fall, groundwater level and groundwater contamination and also used in many number of extremes. The feedforward neural network model was applied to the monthly rain fall and also trained with Levenberg-Marquardt Back Propagation Algorithm for forecasting of it in Varanasi district, Uttar Pradesh. The Regression coefficient (R2) was measured for accuracy of developed neural network model and its value approximately one for each year.
Key words: Artificial Neural Network (ANN), Varanasi district, Rain fall, Feedforward.
INTRODUCTION
Rain fall process is essentially random in nature. It is important role in hydrological cycle. Due to rain fall natural recharge take place and it follows rising of groundwater level. Rainfall forecasting is one of the most difficult and important processes of the hydrologic cycle. This is largely related to the variability it displays over a wide range of scales both in time and space. Flash flooding, being a product of intense rainfall, is a life-threatening phenomenon. Developing a rainfall forecasting and flood warning system for typical catchments is not considered a simple task. Both internal and external characteristics of rainfall field depend on many factors including: pressure, temperature, wind speed and its direction,
JECET; June – August 2013; Vol.2.No.3, 721-729. 722 meteorological characteristics of the catchments and so on. Although a physically-based approach for rainfall forecasting has several advantages, given the short time scale, the small catchments area, and the massive costs associated with collecting the required meteorological data, it is not a feasible alternative in most cases because it involves many variables which are interconnected in a very complicated way1. In recent years, Artificial Neural Networks (ANNs) have become extremely popular for prediction and forecasting in a number of areas, including finance, power generation, medicine, water resources and environmental science7. Artificial Neural Networks (ANNs) started about 47 years ago, when the theory of perceptron was presented by Rosenblatt2 (1962). During the mid-1960s, the interest in the artificial neural network (ANN) decreased, because of the limitations of the theory of perceptron3. Since new ANN paradigms have overcome some of these limitations, the artificial neural network (ANN) has emerged again as an active research area within computer science, engineering, physics and geophysics4.
STUDY AREA
Location & Geographical of the Study Area: The study area extended over Varanasi district in the part of eastern Uttar Pradesh, shown in Fig.1. It is bounded in north by Ghazipur district, in south by Mirzapur district, in east by Chandauli district, in North West by Jaunpur district and in south west by Sant Ravidas Nagar. It situated between latitudes 250 10’ 30’’ to 250 35’ 15’’ North and longitudes 820 40’ 50’’ to 830 12’ 18’’ East and total area approximately 1550 Sq Km. The area is covered by the Survey of India toposheet No.63K/5,63k/15.
Fig. 1: Map of the study area
Topography of the Study Area: The city of Varanasi is located in the middle Ganges valley of North India, in the Eastern part of the state of Uttar Pradesh, along the left crescent-shaped bank of the river Ganges. The river system consists of the mighty Ganga highly revered by Hindus since ages and Gomti, Varuna, Asi, Banganga, Chandra Prabha and Karmanasa are tributaries of the Ganga, that drain the area.
JECET; June – August 2013; Vol.2.No.3, 721-729. 723 Being located in the Indo-Gangetic Plains of North India, the land is very fertile because low level floods in the Ganges continually replenish the soil5.
Geology of the Study Area: The study area is a part of Indo-Gangetic plain which is underlain by Quaternary alluvial sediments of Pleistocene to Recent age. In the study area,however, the unconsolidated sediments from a sequence of clays and sands of various grades. Nodular calcareous concretions (kankars) are at times intercalated with the sands and form potential aquifers at various depths. Shallow aquifers occur principally in clay kankar and meander river deposits. Deep aquifers occur in thick sand layers and have good potentials6.
METHODOLOGY
The following methodology have been applied such as
• Data collection from B.H.U rain gauge station.
• Analyzed the data by software Ms. Excel.
• Construction artificial neural network (ANN) model and also included training, validation and testing artificial neural network (ANN) model.
• After testing artificial neural network (ANN) model, it is utilized in many practical and theoretical applications.
• Plot the curve in between actual and estimated rain fall data.
The flow chart of methodology has been drawn in Fig.2.
Fig. 2: Flow chart of methodology
Source: Seyam, Mohammed;( 2009)7 Groundwater Salinity Modeling Using Artificial Neural Networks Gaza Strip case study (Modified).
JECET; June – August 2013; Vol.2.No.3, 721-729. 724 ARTIFICIAL NEURAL NETWORK (ANN)
The Artificial neural network (ANN) is computational tool based on the biological nervous systems.
Artificial neural network (ANN) is composed of three essentials: neurons, architecture and learning paradigm. Neurons are fundamental elements of a network and are interconnected in a definite rule to form architecture through network weights. The learning paradigm refers to the method that the network weights are adjusted8.
a. Feedforward Neural Network (FNN): The Feedforward neural networks have been applied successfully in many different problems since the advent of the error backpropagation learning algorithm.
This network architecture and the corresponding learning algorithm can be viewed as a generalization of the popular least-mean-square (LMS) algorithm9, 10.
Fig. 3: Architecture of the ANN model for prediction of rain fall
A multilayer perceptron network consists of an input layer, one or more hidden layers of computation nodes, and an output layer. Figure 3 shows a typical feedforward network with one hidden layer consisting of three nodes, four input neurons and one output. The input signal propagates through the network in a forward direction, layer by layer. Their main advantage is that they are easy to handle, and can approximate any input/output map, as established by Hornik et al.11 . The key disadvantages are that they train slowly, and require lots of training data (typically three times more training samples than network weights) 9.
b. Training and Testing with Levenberg-Marquardt Back Propagation Algorithm (LMB): The Levenberg–Marquardt method is a modification of the classic Newton algorithm for finding an optimum solution to a minimization problem. It uses an approximation to the Hessian matrix in the following Newton-like weight update
(1) Where x the weights of neural network, J the Jacobian matrix of the performance criteria to be minimized, a scalar that controls the learning process and e the residual error vector.
When the scalar is zero, Eq. (1) is just the Newton’s method, using the approximate Hessian matrix.
When is large, Eq. (1) becomes gradient descent with a small step size. Newton’s method is faster and
JECET; June – August 2013; Vol.2.No.3, 721-729. 725 more accurate near an error minimum, so the aim is to shift towards Newton’s method as quickly as possible9.
Here, Five years (2003-07) monthly rain fall data have been used with other weather parameters such as maximum temperature, minimum temperature and relative humidity. Because, local weather parameters directly dependent upon rain fall. The target data made up by the rain fall corresponding to the other weather parameters.
The Regression coefficient (R2) was measured for accuracy of the output in selected network and calculated by
Where are the observed data, the calculated data and the number of observations. The best fit between observed and calculated values would have as 11.
RESULTS AND DISCUSSION
The number of hidden layers, learning rate and Regression coefficient ( ) shows in table 1 for each years. The hidden layer varies from 15 to 40. Regression coefficient ( ) have been calculated from Eq.2 and varies from 0.65957 to 0.96439. If the value of regression coefficient ( ) is high then the predicted rain fall values are good fitted with actual rain fall values.
The maximum regression coefficient ( ) is 0.96439 in year 2006 Fig. 4 and in which network having 20 neuron in the hidden layer. In year 2006, the predicted rain fall trend followed closely to the actual rain fall trend closely comparison to other shows in Fig. 5.
In year 2003: R2=0.90373 In year 2004: R2=0.73105
In year 2005: R2=0.65957 in year 2006: R2=0.96439
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In year 2007: R2=0.88913
Fig. 4: Regression Analysis of target and output values
Table 1: Network parameters and Regression coefficient (R2) for the ANN model
Year
Hidden Neuron Layers
Regression coefficient
(R2) Learning rate (η)
2003 15 0.90373 1.00E-08
2004 40 0.73105 1.00E-07
2005 20 0.65957 1.00E-08
2006 20 0.96439 1.00E-08
2007 20 0.88913 0.1
JECET; June – August 2013; Vol.2.No.3, 721-729. 727 In year 2003
In year 2004
In year 2005
In year 2006
JECET; June – August 2013; Vol.2.No.3, 721-729. 728 In year 2007
Fig. 5: Trend of the model ACKNOWLEDGEMENTS
The authors wish to thank the Head of Department of Geophysics, B.H.U, and Varanasi, India for providing the Infrastrure to complete this work and also the staff of B.H.U rain gauge station for providing the data used in this work.
REFERENCES
1. M. Nasseri, K. Asghari and M.J. Abedini. Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Science Direct, 2008, 35: 1415-1421.
2. F. Rosenblatt. Principles of neuro-dynamics: Spartan books, 1962.
3. M.L. Minsky and S. A. Papert Perceptron: MIT Press, 1969.
4. A. Neyamadpour, W.A.T. Abdullah and S.Wan Taib. Inversion of quasi-3D resistivity imaging data using artificial neural networks. Journal of Earth System Science, 2010, 119: 27-40.
5. http://www.dcmsme.gov.in.
6. N.J. Raju, P. Ram and S. Dey. Groundwater Quality in the Lower Varuna River Basin, Varanasi District, Uttar Pradesh. Journal of Geological Society of India, 2009, 73: 178-192.
JECET; June – August 2013; Vol.2.No.3, 721-729. 729 7. S. Mohammed. Groundwater Salinity Modeling Using Artificial Neural Networks Gaza Strip
case study, (Published thesis), 2009 The Islamic University of Gaza High Studies Deanery Faculty of Engineering Civil Engineering Department Water Resources Engineering.
8. X.U. Hai-Lang and W.U. Xiao-Ping. 2-D Resistivity inversion using the Neural Network method. Chinese Journal of Geophysics, 2006, 49: 507-514.
9. N. Daliakopoulos, P. Coulibaly and I.K. Tsanis. Groundwater level forecasting using artificial neural network, Journal of Hydrology, 2004, 309: 229-240.
10. S. Haykin, Neural Networks, A Comprehensive Foundation, second ed. Prentice-Hall, Englewood Cliffs, NJ, 1999.
11. K. Hornik, M. Stinchcombe and M.White. Multilayer feed forward networks are universal approximators, Neural Networks, 1989, 2: 359–366.
*Corresponding Author: R. Kumar; Department of Geophysics, Banaras Hindu University, Varanasi,(U.P.) India
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