Gaussian and Gamma Mixture Model Approach to Rainfall Analysis in Flood-Prone Regions of Karnataka

Abstract

Flood is one of the many natural disasters that affects people's social and economic well-being in India and around the world. The current flood analysis study focusses on Coastal Karnataka regions namely Dakshina Kannada, Udupi, Uttara Kannada, Chikkamagalur, Kodagu, and Shivamogga also known as flood prone regions of Karnataka due to heavy rainfall recorded every year. After being rainfall data collected for 57 years, the southwest monsoon rainfall data for these areas is examined and put through a Gaussian-Gamma mixture (GGM) model. This is because rainfall data has multimodal characteristics and does not follow a Gaussian distribution. Prior presenting the data to the GGM model, the data was normalized in order to do the comprehensive statistical analysis. The Maximum Likelihood estimator method is used to determine the parameters involved in the GGM model. The data is seen to follow the GGM distribution, and the model able to capture the data's moments. With this in view, level crossing statistics are calculated and computed for the data length used at 10% and 20% above normal values using GGM model and Gaussian model. The comparison shows that GGM model is able to capture the flood events of the regions considered for the current study better than the Gaussian model both at 10% and 20% above normal values. For Karnataka's flood-prone areas, this research provides vital insights into flood risk management, supporting proactive disaster mitigation, efficient resource planning, and climate adaption measures.