KIKON, AYILOBENIDODAMANI, B M2026-02-022024https://idr.nitk.ac.in/handle/123456789/18868Drought is a highly damaging natural event having a significant impact on the environment, agriculture, economy and public health resulting in a cascade of vulnerabilities across several sectors. Drought occurs in all climatic zones mainly because of deficit in precipitation for a prolonged period. Every year significant areas and population around the globe are affected by drought which can last anywhere between weeks to years. Understanding the numerous climatic parameters affecting the variability of rainfall and outbreaks of drought is a major scientific challenge. Due to changes in the climate and activities by people, there is a need to understand the various catastrophe causing due to drought and adopt measures to overcome and prevent the drought consequences. Drought prediction emerges as one of the crucial tools that can provide helpful information and may be used to mitigate drought impacts. Meteorological drought is a type of drought that results from inadequate amounts of rainfall in any region. The study has been conducted in the Indian region consisting of thirty-four meteorological subdivisions. The study aims to analyse the rainfall and drought indices trend using the monthly precipitation data from 1958-2017. The Mann-Kendall test has been applied to determine the trends in rainfall and drought indices. The Effective Drought Index (EDI) and Standardized Precipitation Index with 9-month and 12-month timescale are the meteorological drought indices that are assessed using monthly rainfall data. These meteorological drought indices are predicted using machine learning algorithms such as the Genetic Algorithm-Adaptive Neural Fuzzy Inference System (GA-ANFIS), Particle Swarm Optimization-Adaptive Neural Fuzzy Inference System (PSO-ANFIS), and Generalized Regression Neural Network (GRNN), and the obtained results are compared. The Mann-Kendall test results showed a clear indication that rainfall has been consistently decreasing during the study period, leading to water shortages and dry conditions. Understanding both rainfall patterns and drought trends is therefore essential for efficient planning and control of the numerous impacts of drought. The ii machine learning algorithms employed in this work show they are capable of predicting meteorological drought indices under various climatic situations. Based on performance measures such as coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Normalized root mean square error (NRMSE), comparative study of the models shows that hybrid machine learning models (GA-ANFIS and PSO-ANFIS) perform better than the non-hybrid model (GRNN). Notably, it has been observed that, as the timescale for the drought index increases, it shows a better performance with more accuracy of the performance metrics. Based on the study findings, it emphasizes in assessing the rainfall and drought trend could be beneficial in understanding the drought behaviour and identify drought prone locations and develop mitigation strategies to overcome the drought impacts. Overall, this study plays a significant role in understanding the rainfall pattern and its distribution for water management and planning for future water use. Adopting hybrid machine learning algorithms for predicting of meteorological drought indices may provide a better outcome for drought assessment. Also, assessing the historical droughts provides a better understanding and management of past drought occurrences. Future research attempts could be focused on improving drought vulnerability mapping by modelling and probabilistic climate data analysis. Additionally, understanding the dynamics of drought may also be improved by investigating at how drought occurrences begin and terminate. The exploration of alternative hybrid machine learning approaches and the incorporation of additional drought indices could contribute to more robust evaluations in assessing drought conditions.enDroughtEffective drought indexStandardized precipitation indexMann-Kendall testDrought predictionMachine learning algorithmsThesis