• Bharati S. Pochal

Precipitation estimating is significant for some catchment applications, specifically for flood notice frameworks. The fluctuation of precipitation in existence, in any case, renders quantitative anticipating of precipitation incredibly troublesome. The profundity of precipitation and its conveyance in the worldly and spatial measurements relies upon numerous factors, for example, weight, temperature, and wind speed and heading. Because of the unpredictability of the barometrical procedures by which precipitation is produced and the absence of accessible information on the vital worldly and spatial scales, it isn't attainable for the most part to figure precipitation utilizing a truly based procedure model. Ongoing improvements in man-made reasoning and, specifically, those procedures focused on design acknowledgment, be that as it may, give an elective way to deal with creating of a precipitation determining model. Artificial neural systems (ANNs), which play out a nonlinear planning among information sources and yields, are one such procedure. Introduced in this work are the aftereffects of an examination researching the utilization of ANNs to conjecture the spatial dissemination of precipitation for a urban catchment. Three elective sorts of ANNs, in particular multilayer feed forward neural systems, fractional intermittent neural systems, and time defer neural systems, were distinguished, created and, as introduced in this work, found to give sensible expectations of the precipitation profundity one time-step ahead of time. The information prerequisites for and the precision possible from ANN'S.

ANN, Sattelite Pridiction, Rainfall Prediction, K Means Cluster, CNN.

"Analysis of Predicting Rain Fall Rate Using ANN Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.187-193, June-2019, Available :http://www.jetir.org/papers/JETIR1907W28.pdf

"Analysis of Predicting Rain Fall Rate Using ANN Model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp187-193, June-2019, Available at : http://www.jetir.org/papers/JETIR1907W28.pdf