Robotic Perception Amalgamated with Autonomic Computing for Ground Water Level Detection

Ground water detection is a very important problem in environmental science. Apart from manually locating the sites pertaining ground water, there is a need for the automated calculation of probability of occurrence of water in a particular ground area. In this paper a novel approach based on robotic perception amalgamated with autonomic computing is proposed. The attributes comprises of litho logy, geomorphology, soil type, land type, slope and lineament. The information collected is stored in the History table (Case Base). Case base reasoning is performed. The probability of occurrence of ground water is computed as per the Boolean probability functions propounded and the solutions stored as experience in the History table. The robot perceives the given set of external attributes from real time scenarios and stores the data in the microprocessors. In case of partial perception, learning is done and at last the action whether to drill the area for a particular site is performed.