# Designing an Interactive Visual Analytics System for
## 2 Previous Work
[18] developed a data-driven machine learning approach for rainfall prediction. Different ground-based weather features were identified, including temperature, relative humidity, dew point, solar radiation, PWV, and seasonal and diurnal variables, as they may affect precipitation. Although all features contribute to rainfall classification, only a subset, such as precipitable water vapor, solar radiation, and seasonal/diurnal features, are particularly significant for rainfall prediction. They used these key factors to develop the best feature set for the machine learning algorithm. Experimental results demonstrated a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. They found that the proposed method significantly reduces the false alarm rates compared to previous studies, which reported false alarm rates between 60% and 70%. [PERSON] et al. [19] examined the effectiveness of machine learning algorithms in predicting rainfall using the dataset that includes meteorological data such as temperature, humidity, wind direction and speed, atmospheric pressure, and cloud coverage. To improve data quality, they applied various data preprocessing techniques to handle missing values, re-sampling to address data imbalance, encoding categorical features, and detecting outliers. They also performed a feature engineering process to remove irrelevant or redundant attributes. Among the different ML algorithms they evaluated, the results showed that ensemble learning methods, particularly LightGBM, random forest, and XGBoost, demonstrated high accuracy and predictive power on both validation and testing sets. The second research stream focuses on the long-term analysis of precipitation patterns and trends. These studies examine variation in precipitation over time by examining seasonal and annual fluctuations. [PERSON] and [PERSON] [20] examined significant changes in annual and monthly precipitation patterns across different regions of Turkey. They used two nonparametric statistical techniques, the Mann-Kendall and Sen's slope trend test, to analyze a dataset from 96 precipitation stations between 1929 and 1993. The Mann-Kendall test was used to identify significant trends. The Sen's slope estimator was used to determine the trend magnitude. As both techniques do not hold any assumptions about the underlying distribution of data, they are suitable for time series precipitation data analysis. They found significant shifts in monthly trends in January, February, and September, indicating possible seasonal changes in rainfall patterns. Additionally, there was a considerable decline in the annual mean precipitation in western and southern Turkey and along the Black Sea coast. [PERSON] et al. [21] introduced three hybrid methods that combine the innovative trend methodology (ITM) and two extensions--Double ITM (D-ITM) and Triple ITM (T-ITM)--with the Hilbert Huang Transform (HHT) for analyzing and visualizing rainfall trends. This integrated framework isolates long-term trends by minimizing the influence of short-term variability or noise. They collected and examined annual precipitation data of three different hydrological basins in Northern Algeria from 1920 to 2011. They found that the hybrid approaches yielded important insights and revealed hidden trends. They also determined significant wet periods from 1950 to 1975, followed by a long-term drought in the western region of Northern Algeria. Their comparative results demonstrated that the hybrid methods performed better than conventional methods using discrete wavelet transform (DWT) in identifying hidden trends and providing better visualizations. [PERSON] and [PERSON] [22] examined long-term trends, variability, and seasonal changes in rainfall and temperature in Odisha, India. Monthly rainfall records and average maximum and minimum temperatures between 1980 and 2017 were analyzed. They found an overall upward annual rainfall trend, with a notable increase during the monsoon season (June to September).