@article{jcsa20153612,
author={{Kumar, Dixit Prasanna and Archana, Sahoo},
title={Ground Water Prediction Using Bacterial Foraging Optimization Technique},
journal={Journal of Computer Sciences and Applications},
volume={3},
number={6},
pages={172--176},
year={2015},
url={http://pubs.sciepub.com/jcsa/3/6/12},
issn={2328-725X},
abstract={Ground water in the water located beneath the earth¡¯s surface in soil pore spaces and in the patterns of rock formations. The ways to determine the level of ground water is called ground water prediction. The ground water level is the important factors which affect the development of national economy and society. It is needed to predict ground water because in recent times too much water is pumped out from under ground . In the State of Odisha (India), more than 85% of geographical area falls under consolidation formations with low groundwater development status. In places like Puri, Bhubaneswar ground water can be found at around 200ft down but in case of rocky areas like Keonjhar, western Odisha such as Koraput, Titlagarh etc., it is very hard to reach the ground water level which is very deep down the earth surface. Another problem is the authorities from water resource development use to dig unnecessary pot holes in search of ground water without having any information about the availability of water. These pot holes create problems for the local people. It leads to wastage of both time and money of government. So this paper proposes a method through this we can overcome the above problem. In this paper the availability of ground water was predicted by using some prediction techniques like linear regression, multi-linear regression. Regression analysis can be used to model the relationship between one or more independent or predictor variables and a dependent or response variable (which is continuous-valued). In other words, a regression model is a mathematical model that predicts a continuous response variable. And also we are introducing the prediction of ground water by using a nature inspired Bacterial Foraging Optimization (BFO). This technique is proposed by K.M. Passino in 2002 to handle complex problems of the real world . Bacterial Foraging Optimization Algorithm (BFO) is inspired by the foraging and chemotactic phenomenon of bacteria. Bacterial Foraging Optimization (BFO) algorithm is a new class of biologically stochastic global search technique based on the foraging behaviour of E. coli bacteria. This method is used for locating, handling, and ingesting the food. During foraging, a bacterium can exhibit two different actions: tumbling or swimming which can be simulated for predicting the ground level water.},
doi={10.12691/jcsa-3-6-12}
publisher={Science and Education Publishing}
}