Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

Estimation of rainfall quantile is an important step in regional frequency analysis for planning and design any water resources project. Related evaluations accuracy uncertainty help to further assist enhancing the reliability estimates. In this study, therefore, we investigate extreme computed from genetic algorithm-based clustering. Uncertainty assessment explored with prediction quantiles a new spatial Information Transfer Index (ITI) Monte Carlo simulation framework. And, done comparison growth curves at-site each homogenous region. Further, ITI method compared Maximum Likelihood estimation (MLE) optimized by algorithm (GA) check suitability method. Results obtained suggest ITI-based estimates outperformed those The MLE-GA based on was found be better than L-moments, suggesting former as alternative compare Moreover, minimal bias least deviation curve were regions. confidence intervals seen well within bounds normality assumptions. Doi: 10.28991/cej-2021-03091762 Full Text: PDF