Self-Organizing Map Network-Based Soil and Water Conservation Partitioning for Small Watersheds: Case Study Conducted in Xiaoyang Watershed, China

Abstract

Soil and water conservation partitioning (SWCP) considers complex environmental statutes and development demands and serves as a scientific basis for conducting soil erosion management and practice. However, few studies have researched partitioning in small watersheds (< 50 km2), and guidelines for enabling region-specific measures are lacking. In this study, the Xiaoyang watershed located in the red soil region of southern China was selected as a representative small watershed in which to conduct partitioning. The pressure–state–response (PSR) model was used as a framework for establishing an indicator system that included soil erosion sensitivity, the soil erosion condition, and ecosystem services. With three soil and water conservation variables as the input layer, a one-dimensional self-organizing map was applied to identify clusters in the small watershed. The silhouette width was evaluated to determine the optimal number of regions. Based on the associated results, the Xiaoyang watershed was divided into five regions accounting for 82%, 9%, 8%, 2%, and 1% of the total area, respectively. This study provides a framework on which region-specific soil erosion measures can be planned, and it also provides a partitioning method that can be employed in other areas.

Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning

“…Compared with DL algorithms that require a vast of data, one important advantage for SOMs is to conduct steady learning with relatively lower computational resources and calculation costs. Recent research examples of clustering, visualization, recognition, classification, and analyses using SOMs comprise medical system applications [ 28 , 29 , 30 , 31 , 32 ], social infrastructure maintenance [ 33 , 34 , 35 , 36 , 37 , 38 ], consumer products and services [ 39 , 40 , 41 , 42 , 43 ], food and smart farming [ 44 , 45 , 46 ], and recycling and environmental applications [ 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. We employed SOMs and their variants for the task of classification and visualization of mood states.…”