Worldwide, flooding is the leading cause of losses from natural hazards and is responsible for a greater number of damaging events than any other type of natural event. At least one third of all losses due to nature’s forces can be attributed to flooding. . In the past ten years losses amounting to more than 250 billion dollars have had to be born by societies all over the world to compensate for the consequences of floods. Currently , there are growing need to forecast at any location where there is a risk of flood damage.Due to lack of early real time flood detection and tracing natural phenomena like flood occur devastating damage in whole world. Another challenge is forecasting an extreme high danger level flood, in particular one that is more extreme extreme than those contained in the historical record and now judged more likely as a consequence of climate and/or land-use change. This raises issues related to the nature of extreme storms (of convective, orographic and frontal type), the dominant processes and properties shaping the flood response, and problems of model configuration and calibration. Another challenge is the quantification of uncertainty in this estimate and its use in risk level -based decision-making related to invoking flood warnings

That’s why our main objective is to make a web interface by using data from satellite data of NASA other various sources to detect flood zone area .Especially,GRACE RLO6 v2 dataset of NASA have been used.