Forecasting Hydrogen sulfide Level Based Neural Computation

this paper aims to design and implement an environmental monitoring and forecasting system based on neural computing approach. The output information is used for feeding the alarming systems. The data are collected in real-time through pollution monitoring sensors at Sohar region. Air pollution is a serious problem and coming from different sources that can lead to a catastrophic, which is needed to be monitored and controlled. The proposed work is monitored and managed the alerts on the emissions of Hydrogen sulfide (H2S) in Oman. It is forecasting the Level of H2S in the Sohar region based Neural Computation. The SOFM is used to compute and predict the ratio of H2S and then issue an alarm to take the proper decision which helps to implement the necessary precautions. The experiments are giving evidence that the predicted values are closely to true values that gained from real sensors with accuracy of 78% and less MSE of 0.03865