Analyzing Trend Probability and Risk Estimation of Rainfall Pattern over Maharashtra

In meteorology, the prediction and risk analysis of rainfall is one of the foremost concerns. Early prediction of weather has acquired consideration by numerous scientists from different exploration networks because of its impact to the worldwide human existence. The arising profound learning strategies somewhat recently combined with the wide accessibility of huge climate perception information and the approach of data and computer technology innovation have inspired numerous researchers to investigate hidden hierarchical patterns in the enormous volume of climate dataset for climate determining. Several work and many techniques have already been carried out and proposed to predict rainfall. The focus is especially on statistical analysis, machine learning and deep learning techniques to analyze the data and forecast. The study explores profound learning strategies for climate determining. Specifically, proposed the expectation execution of LSTM and ANN models with respect to rain fall prediction. Those models are tried utilizing climate dataset. Forecasting precision of each model is assessed. The consequence of this study expected to add to climate gauging for wide application spaces including flight route to horticulture, the travel industry and early predication of rainfall.