A Gaussian and Gamma mixture model approach to Rainfall analysis in drought-prone regions of Karnataka

This research article presents an in-depth statistical analysis of drought-prone regions in Karnataka, focusing on Bagalkote, Chitradurga, Koppala and Raichur. By using historical data and utilizing Gaussian and Gamma mixture distribution models, this study aims to model drought patterns effectively across these regions. Descriptive statistics and exploratory data analysis were conducted to understand regional drought characteristics, followed by the application of Gaussian and Gamma mixture models to capture the variability and intensity of drought occurrences. Parameters within the models were estimated using the maximum likelihood estimation technique to ensure accuracy and robustness in fitting the distribution to the data. The results highlight the comparative performance of Gaussian and Gamma models, demonstrating the mixture model’s to represent drought intensity and frequency across the regions studied. This study offers valuable insights into drought dynamics in Karnataka, contributing to improved drought risk assessment and informing resource management strategies in affected regions.

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Publication Details

Journal
MAUSAM
Published
2025-09-27
DOI
https://doi.org/10.54302/mausam.v76i4.7014
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
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article

A Gaussian and Gamma mixture model approach to Rainfall analysis in drought-prone regions of Karnataka

Kokila Ramesh, H. R. Kumudha
MAUSAM
Hydrology and Drought Analysis
article

A Gaussian and Gamma mixture model approach to Rainfall analysis in drought-prone regions of Karnataka

Kokila Ramesh, H. R. Kumudha
article en

Abstract

This research article presents an in-depth statistical analysis of drought-prone regions in Karnataka, focusing on Bagalkote, Chitradurga, Koppala and Raichur. By using historical data and utilizing Gaussian and Gamma mixture distribution models, this study aims to model drought patterns effectively across these regions. Descriptive statistics and exploratory data analysis were conducted to understand regional drought characteristics, followed by the application of Gaussian and Gamma mixture models to capture the variability and intensity of drought occurrences. Parameters within the models were estimated using the maximum likelihood estimation technique to ensure accuracy and robustness in fitting the distribution to the data. The results highlight the comparative performance of Gaussian and Gamma models, demonstrating the mixture model’s to represent drought intensity and frequency across the regions studied. This study offers valuable insights into drought dynamics in Karnataka, contributing to improved drought risk assessment and informing resource management strategies in affected regions.

MAUSAMVol. 76(4)
Openalex Percentile: Top 75%
Hydrology and Drought Analysis
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