Advanced inferential statistics and data mining for chlorophyll distribution clustering - Felix Reba; Toha Saifudin; Rimuljo Hendradi (Institute of Advanced Engineering and Science, 2026)
This study proposes an integrated statistical framework to analyze chlorophyll distribution in marine environments by combining probability distribution modeling, goodness-of-fit (GoF) evaluation, and machine learning-based clustering. Eight probability distribution models—half normal, inverse Gaussian, Rician, Birnbaum–Saunders, Nakagami, extreme value, t location-scale, and stable—were evaluated using observational chlorophyll-a data from the Copernicus Marine Service. Model performance was assessed through the Kolmogorov–Smirnov (KS) and Anderson Darling (AD) GoF tests, along with five statistical information criteria. The results indicate that the inverse Gaussian and extreme value distributions consistently offered the best statistical fit and ecological relevance across varying sample sizes. Clustering analysis, performed using the k-means algorithm and validated via the silhouette index, further confirmed the robustness of these two models in forming stable and well-separated clusters. In contrast, the half-normal distribution showed poor performance and instability, especially with smaller sample sizes. The proposed taxonomy and spatial visualizations enable empirical classification of model behavior and support integration into real-time marine decision support systems (DSS) for ecosystem monitoring. Overall, the study contributes to the development of accurate, data-driven analytical tools that aid sustainable marine resource management, aligned with sustainable development goal (SDG) 14 on marine ecosystem protection.