A novel approach using Machine Learning to objectively classify terrestrial and extraterrestrial river networks

Artificial Intelligence (AI) is revolutionizing the field of geomorphology, offering a robust tool for objective and quantitative analyses. This pioneering study proposes an innovative framework based on Machine Learning clustering techniques, capable of classifying drainage patterns into multiple morphological classes. This work follows up on a related study in which an attempt at classifying 156 terrestrial and extraterrestrial (Mars and Titan) river networks was made. Rivers’ outlines are intrinsically noisy, difficult to isolate from the background, and can be ambiguous for the human eye. The previous works have been focused on accurately classifying patterns, using the expertise of morphologists, thus introducing a weak link, the human eye, in the chain. This time, a reliable, automatic, and scalable methodology has been obtained, leveraging computers’ precision, objectivity, and computational power. The HydroRIVERS dataset, a publicly available data bank containing vector data, was utilized in this study. All HydroRIVERS data layers are provided in a geographic projection (latitude/longitude), referenced to the WGS84 datum. Each data layer includes an attribute table with information on the morphometric characteristics of each river reach. The input parameters for the clustering models included morphometric features such as LENGTH_KM, DIS_AV_CMS, ORD_STRA, ORD_CLAS, and ORD_FLOW.
During a preliminary experiment, a local convexity test was conducted to determine the optimal number of clusters (k) to identify the best metric values. This test made sure that the number of clusters with the highest evaluation metric was selected, varying in a closed numeric interval. Each cluster corresponds to a specific river class. Significant results were obtained with k = 6, k = 8, k = 10, and k = 12. Subsequently, the K-Means algorithm was applied, grouping the dataset into distinct clusters based on the morphometric parameters. The results were remarkable, with 10 being the best value for k. The results indicate that the clustering algorithm is able to optimally separate the dataset, producing a high inter-cluster distance and a low intra-cluster distance. The dataset points along the features, as highlighted by the three principal components obtained by performing PCA on the final five-dimensional clustering resulting vector space, are well grouped in relatively small clusters, far away from each other. The next step involves using the centroids obtained from the analysis of the large dataset as a reference for classifying the 155 rivers. In general, the centroids obtained from this kind of Learning could be of great value to the scientific community, establishing a new and innovative way of discerning between different classes of rivers without having to manually analyze and inspect images. This approach promises efficient and accurate classification of both terrestrial and extraterrestrial drainage patterns.