Editorial Board

A Refereed Monthly International Journal of Management

Impact of Exploration of Oil and Gas on Education Scenario in Rajasthan by Neural Network Technique

Author

Dr. Asha Sharma Assistant Professor Department of Accountancy and Statistics Mohanlal Sukhadia University, Udaipur E-mail: drashasharma.sharma07@gmail.com

Umar Akhtar (Master Student) Business administration Allama Iqbal University Islamabad, Pakistan

Introduction

Natural gas and oil is one of the energy resources and past experience suggests that oil exploration and related ventures lead to a multidimensional growth and development of the region and nation as a whole. The natural resources are considered more efficient and appropriate for necessary survey and investigation for the assessment, subsequent planning and implementation of various developmental programmes. Rajasthan is emerging as the next energy hub of country. The State has emerged as a repository of wealth, of crude oil, natural gas and lignite. The extensive occurrences of petroliferous basins in Rajasthan have made it a large potential region for hydrocarbons.

Rajasthan Basin

Rajasthan Basin is a sedimentary basin located in the North West part of India. This sedimentary basin is one of the major sources of hydrocarbons like petroleum and natural gas. This basin has a geographical extent of about 1,26,000 square kilometres. The formations of Rajasthan basin uncomfortably lies over a pre Cambrian Basement.

Petroleum in Rajasthan Basin

Huge reserves of naturally occurring hydrocarbons are expected in clastic and carbonate reservoirs in cambrian to paleocene sequences like limestone and shales capped by intra formational shales and tight limestones. Oil & Natural Gas Corporation (ONGC), Oil India Limited and Focus energy are some of major petroleum companies in this basin Sub-Division of Rajasthan Basin

        Rajasthan basin has been further divided into three sub basins.
	Jaisalmer sub basin or Jaisalmer basin
	Bikaner Nagaur sub basin or BNG basin (after Bikaner Nagaur and Ganganagar, town of Rajasthan)
	Barmer Sanchor sub basin

    

Table 1 Petroleum Production
	Average crude oil	Natural Gas	Total
2010-11	125000	900000	1025000
2011-12	137500	950000	1087500
2012-13	175000	1050000	1225000
2013-14	182500	1250000	1432500
2014-15	187500	1800000	1987500
2015-16	167500	2050000	2217500

EDUCATION SCENARIO IN RAJASTHAN

In elementary education, the State has achieved noticeable progress over the last decade. There are 49,861 Primary Schools (PS) with 161,000 teachers and 51955 Upper Primary Schools (UPS) with 161000 teachers and around 70 lakhs students enrolled in year 2010-11. At present, now in the year 2015-16, there are 42577 Primary Schools (PS) with 117,000 teachers and 81409 Upper Primary Schools (UPS) with 138000 teachers in year 2010-11. Teacher pupil ratio is decreased upto 28 and 14 in primary schools and upper primary school from 32 and 18 in primary schools and upper primary school respectively.

Table 2 Education Institute
Year	Primary Schools	Upper Primary Schools	Secondary Schools	college	Total No. Institutes
2010-11	49861	51955	22561	1312	125689
2011-12	51145	55507	24127	1422	132201
2012-13	53243	56483	24612	1527	135865
2013-14	55111	56106	26613	1516	139346
2014-15	41523	79095	27147	1583	107825
2015-16	42577	81409	27698	1842	110949

Table 3 Enrolment, teachers and teacher pupil ratio of primary schools
Year	Enrolled  Students	Number of teacher	Teacher pupil ratio
	(in Lakh)	(in Lakh)
2010-11	51.51	1.61	32
2011-12	51.27	1.54	33
2012-13	48.67	1.56	31
2013-14	45.01	1.58	28
2014-15	41.18	1.16	26
2015-16	42.5	1.17	28

Table 4 Enrolment, teachers and teacher pupil ratio of Upper primary schools
Year	Enrolled  Students	Number of teacher	Teacher pupil ratio
	(in Lakh)	(in Lakh)
2010-11	19.83	1.61	18
2011-12	20.9	1.15	18
2012-13	20.66	1.19	17
2013-14	20.38	1.15	18
2014-15	19.57	1.42	13
2015-16	21.39	1.38	14

Research Objectives

The purpose of this study is to examine correlation between development of education scenario and Natural gas and oil Industry in western Rajasthan. The potential of Natural Gas and oil will also try to find out to know the impact of recent discovery of oil & gas reserves in Rajasthan. It is tried to find out the change in natural gas and oil in Rajasthan influence on social life of community in near future in western Rajasthan. To analyze education scenario and its development in Rajasthan To find contribution of oil and gas industry on education status of Rajasthan To find out the rising trend of no. of education institute, no. of teachers, No. of students and teacher-pupil ratio

 Hypothesis
In order to realize the above objectives, the following hypothesis has been formulated.

H0 There is positive impact of oil and natural gas industry on education scenario on Rajasthan

Research design & methodology

The researcher, being an external analyst, is depend mainly upon secondary data for the. The exploratory research techniques will have been used for this study and also the study is restricted only to Rajasthan based gas, oil and petroleum. Neural Network To prove the hypothesis statistical technique neural network is used. A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria A computational neural network is a set of non-linear data modeling tools consisting of input and output layers plus one or two hidden layers. The connections between neurons in each layer have associated weights, which are iteratively adjusted by the training algorithm to minimize error and provide accurate predictions. Multilayer Perceptron (MLP) Procedure is applied to measure and predict further study. They map relationships implied by the data. The MLP feed forward architectures, meaning that data moves in only one direction, from the input nodes through the hidden layer of nodes to the output nodes. The PARTITION subcommand specifies the method of partitioning the active dataset into training, testing, and holdout samples. The training sample comprises the data records used to train the neural network. The testing sample is an independent set of data records used to track prediction error during training in order to prevent overtraining. The holdout sample is another independent set of data records used to assess the final neural network. You can specify: ––The relative number of cases in the active dataset to randomly assign to the training sample ––The relative number of cases in the active dataset to randomly assign to the testing sample ––The relative number of cases in the active dataset to randomly assign to the holdout sample ––A variable that assigns each case in the active dataset to the training, testing, or holdout sample

DATA ANALYSIS

TAble 5Case Processing Summary
		N	Percent
Sample	Training	4	66.7%
	Testing	2	33.3%
Valid		6	100.0%
Excluded		0
Total		6

The case processing summary in table 5 shows that 4 cases or 66.7% are assigned to the training sample which is used to train the model and 2 cases are assigned to the testing sample which is used to validate the model

Fg-1 Input, hidden and output layer

Figure 1 gives the network information. It describes the process of working. It works into three layer- input layer, hidden layer, and output layer. It is a complete connected graph of input, hidden layer and output respectively. It also synaptic weight which is categorized as less than 0 and more than0. The layers which are grey in colour have impacted more than 0. These layer describing out of the entire factor which components have more weight or more important.

Network Information
Input Layer	Covariates	1	oil
		2	gas
	Number of Unitsa		2
	Rescaling Method for Covariates		Standardized
Hidden Layer(s)	Number of Hidden Layers		1
	Number of Units in Hidden Layer 1a		5
	Activation Function		Hyperbolic tangent
Output Layer	Dependent Variables	1	primary
		2	upperPRI
		3	Ustudents
		4	Uratio
		5	Pratio
		6	Uteachers
		7	Pteachers
		8	Pstudents
		9	secondary
		10	collge
		11	total
	Number of Units		11
	Rescaling Method for Scale Dependents		Standardized
	Activation Function		Identity
	Error Function		Sum of Squares
a. Excluding the bias unit

Table 6 gives information about the network. It describes the process of working. It works into three layer- input layer, hidden layer, and output layer. It shows there are 2 units working under input layer, 5 units are under hidden layer and, 11 units are working under the output layer.

Table 7 Independent Variable Importance
	Importance	Normalized Importance
oil	.252	33.6%
gas	.748	100.0%

Table 7 Model proves perfectly fit with production of natural gas and oil. Importance ratio of oil is 33.6% whether it is 100% with natural gas.

CONCLUSION

Rajasthan is one of the emerging states as a repository of wealth, of crude oil, natural gas and lignite. The extensive occurrences of petroliferous basins in Rajasthan have made it a large potential region for hydrocarbons. There is close relation between the both of the data. Education in Rajasthan is affected by development in oil and natural gas in Rajasthan. Neural network shows the fitness of model means proves the hypothesis that there is positive impact of oil and natural gas industry on education scenario on Rajasthan.

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