# Designing an Interactive Visual Analytics System for
## 2 Previous Work
Because they only consider rainfall event characteristics, traditional IETD estimation techniques like the autocorrelation coefficient, variation in average events, and coefficient of variation may produce inappropriate IETD values. [PERSON] et al. [29] presented a novel solution to this problem by defining IETD as the time between the end of rainfall and the end of direct runoff, considering drainage basin and rainfall characteristics. The proposed method was applied to the Joong-Rang drainage system to establish an area-IETD relationship curve, which was then used to estimate IETD for other urban drainage systems. Simulation results revealed that the new IETD values showed peak flow rates 11% to 15% higher than those estimated using the traditional Huff's method, which assumed a six-hour IETD. These findings highlight the importance of considering rainfall and basin characteristics in IETD determination. [PERSON] and [PERSON] [30] introduced a semiparametric Bayesian generalized exponential (GE) regression model by integrating parametric and nonparametric components. They also included a principleddistance-based prior for the shape parameter. This prior is intended to shrink the GE distribution towards the exponential distribution to preserve the advantages of the exponential family and the adaptability of the GE model. Extensive simulation experiments were conducted to assess long-term rainfall patterns of 1901-2022 during the monsoon season across the Northern, Middle, and Southern Western Ghats regions in India. They demonstrated that the penalized complexity (PC) prior outperformed the traditional gamma prior in terms of coverage probability. Furthermore, there was a significant decrease in absolute fitting error and estimation bias. These results show the precision and reliability of the proposed model over conventional parametric models. In precipitation data analysis, researchers have broadly used visualization techniques to represent data, patterns, extremes, and trends through basic time series plots, contour maps, and isohyet analysis [31; 32; 33]. [PERSON] [34] established standardized approaches for rainfall visualization in hydrologic modeling that emphasize spatial distribution representation within GIS. [PERSON] et al. [35] performed a study on understanding and improving climate outlook visualizations from NOAA's Climate Prediction Center. By testing various visualization design versions through interviews and visualization diagnostic guidelines, they emphasized the value of visualization diagnostics in improving complex climate data visualization. [PERSON] [36] developed a visualization technique to display changes in various weather parameters over time, demonstrating climate change impacts. They used artificial neural networks (ANNs) and a surface joint method using least squares modeling to identify precipitation patterns. [PERSON] et al. [37] designed a novel visualization approach to representing precipitation and river water level relationships by incorporating scatter plots. Instead of utilizing a traditional map-based visualization, they designed a graph structure mimicking river tributary systems' natural connectivities to illustrate rainfall and river level relationships at the same station across multiple time periods. Although numerous studies have applied visualization techniques to improve precipitation data analysis, their primary consideration is to address specific problems rather than develop a comprehensive visualization framework for high-resolution temporal precipitation analysis. Thus, this study introduces a new interactive visual analytics system for addressing this need. With the system, users can perform interactive exploration to identify precipitation extremes, compare storm events across various timeframes and locations, and identify critical threshold exceedances. As an analytical tool, it allows the selection of meteorological stations of interest, manipulates parameters, and conducts scenario analyses for extreme storm events and their flooding potential.