A Novel Predictive Trend Analytics of Rainfall Data in the North-Eastern Part of Visakhapatnam District, India

Abstract

The predictive trend analytic plays a crucial role in estimating the rainfall pattern that impacts on climate change, especially in water resource management. The cyclones and irregular monsoon patterns are two important resulting in unpredictable rainfall which affects the yield and cropping system in agriculture sector and shows major impact on other diversified sectors leading to negative impact on natural drainage system and urban habitation. The present study area is highly prone to cyclones and rainfall irregularities that show a greater impact in the Water management in the Northern part of Visakhapatnam. Present work focuses on the predictive trend analysis of the rainfall data for 38 years from 1982 to 2020 in the study area. By considering monthly, annually, seasonally, annual average rainfall data and a non-parametric analysis the total annual rainfall and seasonal rainfall is estimated using Mann Kendall and Sen’s slope trend analysis algorithms. The predictive calculation results shows that trend analysis performance of Mann Kendall and Sen’s slope values of positive and negative trends. Based on the results, it is predicted that the rainfall pattern in study area may be mostly irregular in nature with significant variability for the next few years also.