Rainfall Data Analysis of Iran using Complex Networks View

Rainfall Zoning is one of the most significant applications in hydro-climatic science. The investigation of these regions helps us to better interpret the functional mechanism of the climatology. A popular way to detect these regions is to use a typical clustering algorithm like K-means on spatial features of the data, But it’s better to detect the zones based on the rainfall data because temporal features of rainfall data, unlike its spatial features, can cause a better result in clustering these data types. The most challenging part while using temporal data is to apply them in the presence of missing values. Here, applying a typical clustering method due to high missing values as a whole block on these data is not proper or maybe even impossible. We implemented a clustering on Iran’s rainfall dataset and one of the most disturbing facts about this dataset was its missing values and to carry out these missing data we demanded to change the data type. To overcome this missing value problem in data, we used a method named "Event synchronization" that could give appropriate similarity for temporal data with high missing values. By this approach, the data with high missing value could be converted to a network. Then by adopting the state-of-the-art community detection algorithm we detected the most related points to each other as rainfall clusters, and we’ll see the promising results at the end. The nature of our real-world data can prove the results.