  Human fatalities are reported due to the excessive proportional presence of
hazardous gas components in the manhole, such as Hydrogen Sulfide, Ammonia,
Methane, Carbon Dioxide, Nitrogen Oxide, Carbon Monoxide, etc. Hence,
predetermination of these gases is imperative. A neural network (NN) based
intelligent sensory system is proposed for the avoidance of such fatalities.
Backpropagation (BP) was applied for the supervised training of the neural
network. A Gas sensor array consists of many sensor elements was employed for
the sensing manhole gases. Sensors in the sensor array are responsible for
sensing their target gas components only. Therefore, the presence of multiple
gases results in cross sensitivity. The cross sensitivity is a crucial issue to
this problem and it is viewed as pattern recognition and noise reduction
problem. Various performance parameters and complexity of the problem
influences NN training. In present chapter the performance of BP algorithm on
such a real life application problem was comprehensively studied, compared and
contrasted with the several other hybrid intelligent approaches both, in
theoretical and in the statistical sense.
