A Novel Approach to Analysis District Level Long Scale Seasonal Forecasting of Monsoon Rainfall in Andhra Pradesh and Telangana
India is a nation which purely relies on agriculture, so rainfall prediction is very important for agriculture to make crop management decisions. Long scale forecast of rainfall during monsoon season (southwest and northeast), at the spatial firmness of a district, could serve as a significant comment to the agricultural community to take better decisions in yield management. Such forecasts are not producing efficient results which are available now. In this paper rainfall, crops and soil data of Andhra Pradesh (AP) & Telangana (TS) states are gathered to analyze rainfall patterns based on soil for crop management. Variety of crops needs adequate rainfall based on their different categories. In this paper, we are proposing a model which relates the analysis of rainfall patterns, soil types and several crops grown in two states on seasonal wise. We are experimenting with last 12 years of rainfall data, a variety of crops grown in different seasons and identifying the average rainfall needed for distinct of crop types. DBSCAN clustering algorithm used to determine the rainfall patterns as low and high density. The proposed system serves as a tool to explore the rainfall patterns. The statistical results show that proposed model could enhance those effectiveness and exactness.
